[ { "productId": "amd-mi355x", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "A live llms.txt file was confirmed at rocm.docs.amd.com/llms.txt with a 200 response, providing an agent-readable entry point into ROCm documentation, and the runtime probe confirms the docs surface is crawlable without a browser. Missing for 10: no evidence of per-page markdown/.md variants (404 on that probe), no OpenAPI/agent tool spec, and no confirmation that agent-oriented docs cover the MI355X product pages themselves rather than just ROCm software.", "evidenceIds": [ "amd-mi355x-probe-1", "amd-mi355x-probe-rt-1", "amd-mi355x-probe-2", "amd-mi355x-probe-3" ] }, { "productId": "amd-mi355x", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware GPU accelerator; 'setting up autonomous background automations' is an application/agent-orchestration capability, not a fair axis for a hardware accelerator product category. The evidence covers job scheduling (Spur) and cluster management tools, but these are infrastructure/ops tools, not user-facing autonomous automation setups.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-headless", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "ROCm/Instinct docs show clear headless-automation building blocks — AMD Container Toolkit for Docker, GPU Operator for Kubernetes, Spur job scheduler (Slurm-compatible), and Cluster/ROCm Validation Suites for automated testing — all of which support running GPU workloads without a UI in CI/cluster pipelines. Missing for 10: no first-party CI pipeline example (e.g., GitHub Actions/GitLab runner config) or independent hands-on report confirming headless CI use in practice.", "evidenceIds": [ "amd-mi355x-docs-9", "amd-mi355x-docs-11", "amd-mi355x-docs-12", "amd-mi355x-docs-13", "amd-mi355x-docs-8" ] }, { "productId": "amd-mi355x", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware accelerator (GPU) with a software/driver stack (ROCm); MCP server plug-in for tool use is an AI-agent/application-layer concept that doesn't apply to a hardware product's own capabilities. This is a category error—no GPU hardware ships MCP server support directly.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware GPU accelerator product, not an agent or agentic tool; connecting an AI agent via an official MCP server is a category mismatch for a hardware accelerator's product axis.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-official-cli", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "AMD ships official CLI tooling in its ecosystem — AMD SMI for GPU management and Spur, explicitly described as an 'AI-native job scheduler' with Slurm-compatible CLI — but these are infrastructure/ops CLIs rather than a CLI aimed at AI-native development or agentic workflows. Missing for 10: evidence of a CLI specifically designed for AI-agent/dev workflows (e.g., code generation, model interaction, agent orchestration) and independent hands-on confirmation of these CLIs' AI-native usability.", "evidenceIds": [ "amd-mi355x-docs-7", "amd-mi355x-docs-11", "amd-mi355x-docs-13" ] }, { "productId": "amd-mi355x", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "AMD documents extensive low-level APIs for driving the GPU (HIP runtime, ROCm libraries, AMD SMI, metrics exporter) and even exposes an llms.txt for machine consumption, but there is no unified, documented public API (e.g., OpenAPI/REST spec) that an AI-native agent could call to drive the product — probes for openapi.json/swagger.json all 404. missing for 10: a formal public API spec/SDK reference for agentic automation, evidence of programmatic/agent-driven control beyond developer-level HIP/ROCm libraries, independent confirmation of agent usage.", "evidenceIds": [ "amd-mi355x-docs-4", "amd-mi355x-docs-7", "amd-mi355x-docs-10", "amd-mi355x-probe-1", "amd-mi355x-probe-3" ] }, { "productId": "amd-mi355x", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware GPU accelerator product; issuing scoped/least-privilege API credentials for an agent is an API/identity-management concern that applies to SaaS platforms or agent frameworks, not to a physical accelerator's product axis.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "agentic-sdks", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "AMD ships extensive official ROCm SDK documentation covering HIP runtime, OpenMP, math/communication libraries, container toolkit, framework integrations (vLLM, SGLang), and cluster/scheduling tools, with docs confirmed live and crawlable. This is a strong official SDK ecosystem AI-native developers can build against, though it lacks an OpenAPI/programmatic API spec and independent third-party validation of SDK quality beyond skepticism about software support. Missing for 10: machine-readable API spec (openapi probe 404s), independent hands-on developer corroboration of SDK usability.", "evidenceIds": [ "amd-mi355x-docs-1", "amd-mi355x-docs-2", "amd-mi355x-docs-3", "amd-mi355x-docs-4", "amd-mi355x-docs-5", "amd-mi355x-docs-13", "amd-mi355x-probe-1", "amd-mi355x-probe-rt-1" ] }, { "productId": "amd-mi355x", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a GPU hardware product with a software/driver stack, not a service exposing subscribable events; webhooks are a wrong axis for a hardware accelerator.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "This is a hardware accelerator with ROCm software docs but no evidence of an interactive API reference with runnable examples — the OpenAPI probe returned 404s on all candidate paths and no interactive playground/notebook reference is mentioned.", "evidenceIds": [ "amd-mi355x-probe-3", "amd-mi355x-probe-2" ] }, { "productId": "amd-mi355x", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack includes a direct probe showing all standard OpenAPI/Swagger endpoints return 404 on AMD's ROCm docs site, and no other citation mentions a machine-readable API spec for MI355X's software stack or management tools.", "evidenceIds": [ "amd-mi355x-probe-3", "amd-mi355x-probe-2" ] }, { "productId": "amd-mi355x", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware accelerator, not an application/service with a user-facing sandbox-vs-production data separation concept; this story targets SaaS/agent platforms and is a category mismatch for a GPU product.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers ROCm/Instinct software stack (HIP, libraries, tools) but nowhere documents API versioning semantics or a deprecation policy; probes explicitly show no OpenAPI/spec discoverable. Axis is plausible for the ROCm developer stack but unevidenced.", "evidenceIds": [ "amd-mi355x-docs-1", "amd-mi355x-probe-3", "amd-mi355x-probe-1" ] }, { "productId": "amd-mi355x", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "The ROCm/Instinct docs point to inference frameworks (vLLM, SGLang) that natively support batched/continuous-batch inference, implying MI355X can process many requests or items in bulk, and GPU partitioning/cluster tooling suggest large-scale parallel job handling. However, there is no direct documentation of a bulk-operation API, batch job submission interface, or AI-native bulk-processing workflow specific to MI355X itself — it's inferred through third-party software rather than demonstrated first-party capability. Missing for 10: explicit bulk/batch API documentation, benchmarked throughput for batch workloads, and independent verification of batch processing at scale.", "evidenceIds": [ "amd-mi355x-docs-3", "amd-mi355x-docs-2", "amd-mi355x-docs-6" ] }, { "productId": "amd-mi355x", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware GPU accelerator; event-driven rule automation is an application/orchestration-layer concern, not a fair axis for a GPU product itself. Nothing in the evidence (monitoring, metrics exporter, scheduler) constitutes user-defined event-trigger rules, so this is a category mismatch rather than a gap.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "automation-scheduled-jobs", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "AMD ships 'Spur', described as an AI-native, Slurm-compatible job scheduler with GPU-first scheduling for Instinct clusters, which implies workflow/job scheduling capability, but the evidence never explicitly confirms recurring/cron-style job scheduling or automation-depth features. Missing for 10: documentation on recurring/cron scheduling semantics, workflow orchestration examples, and independent/hands-on validation of Spur's scheduling capabilities.", "evidenceIds": [ "amd-mi355x-docs-11" ] }, { "productId": "amd-mi355x", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware accelerator; 'versioning, reviewing, rolling back automations' is an application/workflow-orchestration axis that doesn't apply to a GPU product's own capabilities—no automation/workflow layer is claimed here.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "compute-toolchain-support", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "ROCm documentation explicitly covers HIP runtime, libraries, frameworks, and inference stacks (vLLM, SGLang) targeting Instinct GPUs, and Instinct-specific docs list HIP C++, OpenMP, and other toolchain components for the MI355X line, confirming it as a supported ROCm/HIP target. missing for 10: no explicit MI355X-named code sample or compatibility matrix entry pinpointing this exact SKU rather than the Instinct family generally.", "evidenceIds": [ "amd-mi355x-docs-1", "amd-mi355x-docs-3", "amd-mi355x-docs-4", "amd-mi355x-docs-5", "amd-mi355x-probe-rt-1" ] }, { "productId": "amd-mi355x", "storyId": "creator-media-acceleration", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "datacenter-scale-out", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Evidence documents rack-scale deployment tooling (Kubernetes GPU Operator, Container Toolkit, Cluster Validation Suite, Spur scheduler, SR-IOV, AMD SMI) and high memory bandwidth (8TB/s HBM3E), supporting datacenter-scale operations, but no citation explicitly documents the GPU-to-GPU interconnect fabric (e.g., Infinity Fabric link topology or scale-up fabric analogous to NVLink) that the story specifically calls out. Community sources focus on compute/memory comparisons, not interconnect topology, so this axis is only partially evidenced. Missing for 10: explicit Infinity Fabric/interconnect bandwidth specs and multi-GPU topology documentation, independent multi-node training benchmarks confirming interconnect scaling.", "evidenceIds": [ "amd-mi355x-docs-9", "amd-mi355x-docs-11", "amd-mi355x-docs-12", "amd-mi355x-docs-13", "amd-mi355x-docs-14", "amd-mi355x-docs-17", "amd-mi355x-comm-2" ] }, { "productId": "amd-mi355x", "storyId": "efficient-sustained-workloads", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack lacks any documented TDP/power envelope specs or independent performance-per-watt benchmarking for the MI355X; community sources focus on raw throughput and memory bandwidth comparisons (chipsandcheese, wafer.ai) but never normalize for power draw. Missing for 10: official power envelope/TDP documentation, independent perf-per-watt benchmarks, sustained-workload thermal/power test data.", "evidenceIds": [ "amd-mi355x-comm-1", "amd-mi355x-comm-2", "amd-mi355x-comm-4" ] }, { "productId": "amd-mi355x", "storyId": "high-refresh-4k-gaming", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "linux-first-class", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "ROCm's entire documented stack (HIP, container toolkit, Kubernetes GPU operator, AMD SMI, cluster validation) is Linux-native and independent hands-on benchmarking (chipsandcheese architecture deep-dive, HN wafer.ai comparisons) treats MI355X as a real, testable part. However, no evidence cites explicit Linux kernel driver release notes/versioning or independent Linux-specific driver validation reports. Missing for 10: explicit Linux driver release notes/changelog, independent third-party Linux driver-level testing (not just architecture/benchmark commentary), confirmation that cited benchmarks ran on Linux.", "evidenceIds": [ "amd-mi355x-docs-1", "amd-mi355x-docs-4", "amd-mi355x-docs-13", "amd-mi355x-docs-9", "amd-mi355x-comm-1", "amd-mi355x-comm-4" ] }, { "productId": "amd-mi355x", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs confirm ROCm-based inference stack support for vLLM and SGLang, plus explicit MXFP6/MXFP4 low-precision datatype support and large HBM3E memory for LLM inference. However, the story also asks about TensorRT-LLM and llama.cpp support, and explicit FP8 support, none of which appear in the evidence pack. missing for 10: TensorRT-LLM support evidence, llama.cpp support evidence, explicit FP8 precision documentation, independent benchmarks validating inference throughput on these stacks.", "evidenceIds": [ "amd-mi355x-docs-3", "amd-mi355x-docs-17", "amd-mi355x-docs-1", "amd-mi355x-docs-2" ] }, { "productId": "amd-mi355x", "storyId": "memory-spec-bandwidth", "verdict": "partial", "quality": 6, "confidence": "high", "rationale": "AMD's official product page confirms capacity (288GB), memory type (HBM3E), and bandwidth (8TB/s) for the MI355X, corroborated independently by chipsandcheese's comparison data. However, no evidence anywhere states the memory bus width. missing for 10: published bus width (bits) for the HBM3E memory subsystem.", "evidenceIds": [ "amd-mi355x-docs-17", "amd-mi355x-comm-2" ] }, { "productId": "amd-mi355x", "storyId": "ml-framework-support", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "AMD documents ROCm-enabled framework support with specific recipes for PyTorch-adjacent ML stacks and inference frameworks (vLLM, SGLang) tied to Instinct GPUs, backed by a live, crawlable documentation surface. Missing for 10: an explicit official PyTorch build/support-matrix page or version-compatibility table directly citing PyTorch, and independent hands-on confirmation of PyTorch working smoothly on MI355X specifically (only general software-support skepticism exists in community commentary).", "evidenceIds": [ "amd-mi355x-docs-2", "amd-mi355x-docs-3", "amd-mi355x-docs-1", "amd-mi355x-probe-rt-1", "amd-mi355x-comm-6" ] }, { "productId": "amd-mi355x", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack extensively documents ROCm's open-source user-space stack (libraries, runtimes, tools, monitoring, Kubernetes operator) but never mentions the amdgpu kernel driver, its open-source licensing, or upstream Linux kernel inclusion — the specific claim this story asks about is absent. Missing for 10: any vendor documentation of open-source kernel modules, upstream kernel driver support, or distro-inclusion status for the MI355X.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware GPU accelerator controlled via APIs, CLIs, and drivers (ROCm, HIP, AMD SMI); it has no product UI whose functionality would need to be mirrored by an API. The UI/API parity axis is a category error for this type of product.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a GPU hardware accelerator, not a data-hosting SaaS/platform that stores user data to export; there is no 'user data' corpus to export and leave with, so this openness/data-portability axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack describes ROCm documentation, libraries, and tools but never states an open-source license or provides a link to source code repositories; no license terms are mentioned anywhere in the pack.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "openness-self-host", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "The MI355X is sold as on-prem hardware, and AMD provides a full deployment stack for self-hosting: ROCm runtime/libraries, GPU Operator for Kubernetes, AMD Container Toolkit for Docker, Cluster Validation Suite, Device Metrics Exporter, and the Spur scheduler, all documented for running the GPU in a customer-owned datacenter. Community commentary corroborates real-world self-hosted comparisons (e.g., wafer.ai benchmarks) confirming the hardware is deployed and operated independently by third parties. Missing for 10: independent hands-on report specifically walking through a full self-host bring-up (rather than benchmark-only) confirming ease of deployment.", "evidenceIds": [ "amd-mi355x-docs-9", "amd-mi355x-docs-13", "amd-mi355x-docs-12", "amd-mi355x-docs-10", "amd-mi355x-docs-11", "amd-mi355x-comm-4" ] }, { "productId": "amd-mi355x", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware GPU accelerator sold to data centers/cloud providers; data residency/region selection is a deployment/cloud-service concern controlled by whoever operates the infrastructure, not a capability of the chip or its software stack itself. This is a category error for a hardware product axis.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware accelerator/chip product, not a data-handling SaaS or service with user data retention policies; data retention/deletion controls are a category error for a GPU hardware product's own axis (though the operator running workloads on it would manage such policies).", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "MI355X is a hardware accelerator, not a hosted service or SaaS product that collects user telemetry/usage data in a way that requires an opt-out; software-side telemetry opt-out is not a fair axis for a GPU chip's evidence pack.", "evidenceIds": [] }, { "productId": "amd-mi355x", "storyId": "run-70b-local-llm", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "AMD publishes 288GB HBM3E capacity and 8TB/s bandwidth for MI355X, far exceeding what's needed for 70B-class quantized inference, and documents vLLM/SGLang inference stacks for deployment; independent analysis corroborates the memory capacity/bandwidth lead over competing accelerators. Missing for 10: no direct hands-on benchmark or published throughput numbers specifically for a 70B model at a given quantization on this GPU.", "evidenceIds": [ "amd-mi355x-docs-17", "amd-mi355x-docs-3", "amd-mi355x-comm-2" ] }, { "productId": "amd-mi355x", "storyId": "tensor-throughput-disclosed", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence includes only bare marketing multipliers (e.g., 'Up to 2.2X AI performance') and mentions of supported datatypes (MXFP6/MXFP4) without any published absolute TFLOPS/TOPS figures broken out by precision (FP8/FP6/FP4/BF16) or with/without sparsity — exactly the kind of unqualified claim the story asks to avoid. Community sources discuss architectural comparisons qualitatively but never cite AMD's actual per-precision throughput spec table.", "evidenceIds": [ "amd-mi355x-docs-15", "amd-mi355x-docs-17", "amd-mi355x-comm-1" ] }, { "productId": "amd-mi355x", "storyId": "upscaling-frame-generation", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "A direct probe confirms rocm.docs.amd.com/llms.txt returns HTTP 200 with structured content pointing to sub-project docs, giving an agent a genuine machine-readable entry point into the Radeon/ROCm software docs relevant to this GPU. Missing for 10: no evidence of llms.txt coverage at finer granularity (e.g., per-page) and no OpenAPI/agent API surface (which 404s), so agentic doc access is present but not comprehensive.", "evidenceIds": [ "amd-rx-9070-xt-probe-1", "amd-rx-9070-xt-probe-2", "amd-rx-9070-xt-docs-1" ] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a GPU hardware product; autonomous background automation is an application/software-orchestration capability, not something a graphics card ships itself. The evidence only covers driver/software stack support (ROCm, PyTorch) for running AI workloads, not automation/agent orchestration features.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-headless", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows ROCm/Linux support with PyTorch, vLLM, and Llama.cpp for compute workloads (amd-rx-9070-xt-docs-2, -10, -11), which implies the GPU can be used for automated inference/training pipelines without a display, but there is no explicit documentation of headless operation, Docker/CI runner support, or automation-specific tooling. Missing for 10: explicit headless-mode docs, CI/container integration guides, and any hands-on evidence of running in automated pipelines.", "evidenceIds": [ "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-7" ] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RX 9070 XT is a GPU hardware product, not an agent, app, or platform that could plug in MCP servers; this axis is a category error for a graphics card.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a GPU hardware product; MCP server connectivity is a software/agent-role axis unrelated to a graphics card's capabilities, so the story does not apply.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "AMD documents a public software stack (ROCm) with APIs/libraries (HIP, PyTorch/TensorFlow/JAX/ONNX/vLLM/llama.cpp integration) that let developers programmatically drive the GPU for AI workloads, and llms.txt is served for documentation discovery. However, there is no dedicated machine-callable REST/OpenAPI interface — the openapi probe returned 404 on all candidate paths — so an AI agent cannot invoke a structured public API directly, only use ROCm's compiled libraries/frameworks. Missing for 10: a documented REST/OpenAPI or similarly agent-consumable API endpoint, independent confirmation of programmatic control beyond framework bindings.", "evidenceIds": [ "amd-rx-9070-xt-docs-1", "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-probe-1", "amd-rx-9070-xt-probe-2" ] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This story concerns API credential scoping for agent access control, which applies to SaaS/platform services—not a consumer GPU hardware product like the RX 9070 XT. This is a category error/wrong axis for a graphics card.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-sdks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "AMD provides official ROCm SDK documentation for the RX 9070 XT enabling development with PyTorch, TensorFlow, JAX, ONNX, vLLM, and llama.cpp on both Windows and Linux, with an llms.txt confirming machine-readable docs availability. Missing for 10: independent hands-on developer confirmation of SDK stability/completeness, and no evidence of API/openapi endpoints for programmatic integration.", "evidenceIds": [ "amd-rx-9070-xt-docs-1", "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-9", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-13", "amd-rx-9070-xt-probe-1" ] }, { "productId": "amd-rx-9070-xt", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RX 9070 XT is a GPU hardware product; webhooks/event subscriptions are a software service API concept unrelated to a graphics card's function. This axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "api-interactive-docs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RX 9070 XT is a physical GPU product, not an API/SaaS product with a callable API reference; this axis targets developer-facing API docs and does not apply to a hardware product's own interface (ROCm software docs are a separate ecosystem artifact, not an interactive API reference for the GPU itself).", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "api-machine-spec", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RX 9070 XT is a consumer GPU hardware product, not an API/service product; a machine-readable API spec is a category error for this kind of product.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This story concerns sandboxed testing environments isolated from production data, which is a software/platform concern, not applicable to a GPU hardware product like the RX 9070 XT.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product is not itself an API service; versioned APIs with deprecation policies apply to software platforms/services, not to a graphics card as a category.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a GPU hardware product; defining automation rules that trigger actions on events is a software/platform automation feature, not something a graphics card itself provides—this is a category mismatch (wrong axis).", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This story concerns versioning/reviewing/rolling back automations, an application/workflow-orchestration feature; a GPU hardware product has no such capability layer to evaluate.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "compute-toolchain-support", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "ROCm documentation explicitly lists RX 9000 series (including 9070 XT) as a supported target on both Windows and Linux, with PyTorch, TensorFlow, JAX, ONNX, vLLM, and Llama.cpp support, plus a stated migration path to AMD Instinct datacenter GPUs. missing for 10: independent hands-on developer corroboration of ROCm workflows on this specific card beyond vendor docs, and no mention of CUDA compatibility layer maturity/limitations.", "evidenceIds": [ "amd-rx-9070-xt-docs-1", "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-9", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-13" ] }, { "productId": "amd-rx-9070-xt", "storyId": "creator-media-acceleration", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "AMD's product page explicitly documents AV1 encode/decode support for the RX 9070 XT, which is relevant to creator workflows, but there is no mention of HEVC encode/decode support, no ISV-certified professional driver program (that's typically reserved for Radeon Pro cards), and no explicit creator-app acceleration claims (e.g., Premiere, DaVinci Resolve, OBS integration). Missing for 10: HEVC encoder/decoder documentation, ISV/professional certification claims, named creator-app acceleration partnerships or benchmarks.", "evidenceIds": [ "amd-rx-9070-xt-docs-8" ] }, { "productId": "amd-rx-9070-xt", "storyId": "datacenter-scale-out", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence only shows ROCm software compatibility and a vague note that the same stack 'also supports' Instinct/CDNA datacenter accelerators, but nothing documents NVLink/Infinity Fabric interconnect, multi-GPU scaling, or rack-scale deployment for the RX 9070 XT itself, which is a single consumer desktop card without such interconnects. missing for 10: any documentation of multi-GPU interconnect (NVLink/Infinity Fabric) for this card, multi-GPU system support, rack-scale deployment guidance.", "evidenceIds": [ "amd-rx-9070-xt-docs-13", "amd-rx-9070-xt-docs-4" ] }, { "productId": "amd-rx-9070-xt", "storyId": "efficient-sustained-workloads", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence covers ROCm software support, framework compatibility, and general GPU features, but there is no documented power envelope data or independent performance-per-watt testing for sustained ML workloads on this card. missing for 10: TDP/power envelope specs for sustained ML loads, independent perf-per-watt benchmarks, thermal/power throttling behavior under long-running compute jobs.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "high-refresh-4k-gaming", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Only one independent source (TechPowerUp) confirms the RX 9070 XT delivers competitive raster/ray-tracing performance versus similarly priced NVIDIA cards, but it doesn't specifically address 4K high-refresh benchmarks or vendor FPS claims. Missing for 10: explicit vendor 4K/144Hz+ performance claims, detailed independent 4K benchmark numbers across multiple games, and confirmation of sustained high-refresh framerates.", "evidenceIds": [ "amd-rx-9070-xt-comm-1" ] }, { "productId": "amd-rx-9070-xt", "storyId": "linux-first-class", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "AMD's official docs explicitly list Linux x86-64 as a supported OS and provide detailed ROCm Linux documentation for PyTorch/TensorFlow/JAX/ONNX, vLLM, and llama.cpp on Radeon 9000-series GPUs, showing genuine first-class Linux driver/software support. However, the pack lacks independent hands-on Linux testing or benchmarks of the RX 9070 XT specifically (the only community citation is a general Windows-oriented performance review, not Linux-focused). Missing for 10: independent/third-party Linux driver stability or performance testing of this specific card, and any community confirmation of ROCm functionality on this GPU outside vendor docs.", "evidenceIds": [ "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-7", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-3" ] }, { "productId": "amd-rx-9070-xt", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "ROCm docs explicitly document serving-stack support for this Radeon line (vLLM 'Full support', llama.cpp 'Supported for efficient inference', plus PyTorch/TensorFlow/JAX/ONNX), but there is no mention of low-precision FP8/FP4 inference formats for the RX 9070 XT, and TensorRT-LLM is an NVIDIA-only stack so is not applicable here. Missing for 10: explicit FP8/FP4 quantization support documentation for this card, independent hands-on benchmarks confirming these serving stacks actually run well on RX 9070 XT.", "evidenceIds": [ "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-9" ] }, { "productId": "amd-rx-9070-xt", "storyId": "memory-spec-bandwidth", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence pack contains no VRAM capacity, memory type (GDDR6), bus width, or bandwidth figures for the RX 9070 XT—only ROCm software ecosystem claims and generic product page snippets unrelated to memory specs.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "ml-framework-support", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "AMD's official ROCm docs explicitly list RX 9000 series support for PyTorch (Windows and Linux), TensorFlow, JAX, ONNX, vLLM, and Llama.cpp, with a documented support matrix and OS support (Windows/Linux) confirmed on AMD's product page. Missing for 10: independent hands-on confirmation of install success/version compatibility and no community corroboration of real-world PyTorch training/inference workflows on this specific card.", "evidenceIds": [ "amd-rx-9070-xt-docs-1", "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-9", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-7" ] }, { "productId": "amd-rx-9070-xt", "storyId": "open-source-driver", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "AMD documents ROCm as a primarily open-source stack with Linux support and lists RX 9000-series compatibility, and Linux x86 64-bit OS support is confirmed, but there is no explicit vendor documentation of open-source kernel driver components (e.g., amdgpu upstream kernel module) specific to RX 9070 XT or a clear statement of which parts of the stack are closed-source firmware/blobs. missing for 10: explicit vendor confirmation of upstream open-source kernel module support for this specific GPU, independent/hands-on corroboration of open driver functioning on mainline Linux kernels.", "evidenceIds": [ "amd-rx-9070-xt-docs-3", "amd-rx-9070-xt-docs-7", "amd-rx-9070-xt-docs-2" ] }, { "productId": "amd-rx-9070-xt", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a GPU hardware product, not a service with a UI/API duality; the story asks about API-vs-UI parity, which is a category error for a physical graphics card (software stacks like ROCm are separate products).", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This story concerns data export/portability, which applies to SaaS/data platforms, not a consumer GPU hardware product; a GPU does not hold user data to export.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "openness-open-license", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "AMD's ROCm docs claim the stack is a 'primarily open-source ecosystem' giving users freedom to inspect and customize the software, and this is corroborated by an accessible llms.txt docs endpoint, but this is the accompanying software stack, not the GPU's actual hardware/firmware source, and 'primarily' implies some closed-source components remain undisclosed. Missing for 10: explicit repository/license pointer for the actual open-sourced source code, confirmation of what portions (drivers, firmware) are closed, and independent hands-on verification of source availability.", "evidenceIds": [ "amd-rx-9070-xt-docs-3", "amd-rx-9070-xt-probe-1" ] }, { "productId": "amd-rx-9070-xt", "storyId": "openness-self-host", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "As a discrete GPU, the RX 9070 XT is inherently self-hosted hardware; AMD's docs explicitly position a local Radeon workstation as a secure, economical alternative to cloud-based AI solutions, backed by open ROCm stack support for PyTorch/TensorFlow/JAX/ONNX/vLLM/llama.cpp on both Windows and Linux. Missing for 10: independent hands-on validation of a fully self-hosted AI stack setup, and more detailed first-party self-hosting/deployment guides beyond general ROCm docs.", "evidenceIds": [ "amd-rx-9070-xt-docs-12", "amd-rx-9070-xt-docs-4", "amd-rx-9070-xt-docs-13", "amd-rx-9070-xt-docs-2", "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11" ] }, { "productId": "amd-rx-9070-xt", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers ROCm/software ecosystem support, AMD Software features, and general performance comparison, but contains no mention of TDP/TGP figures, PCIe power connector requirements, or cooling/case guidance for the RX 9070 XT. This is a fair axis for a discrete GPU, but nothing in the pack substantiates it.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RX 9070 XT is a consumer GPU hardware product, not a data storage/hosting service; data residency/region selection is not an applicable axis for locally-installed hardware.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product is not a data-processing service that retains user data; data retention/deletion controls are a SaaS/cloud-service axis, not applicable to a local GPU/driver stack.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a hardware product (GPU); telemetry opt-out settings would belong to bundled driver software, not a fair axis for evaluating the GPU itself as an AI-native product capability.", "evidenceIds": [] }, { "productId": "amd-rx-9070-xt", "storyId": "run-70b-local-llm", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack never states the RX 9070 XT's actual VRAM capacity or memory bandwidth; the one VRAM figure mentioned (\"up to 48GB\") is a generic Radeon workstation-GPU claim, not this card's spec, and there is no claim or benchmark showing a 70B-class quantized model running on this GPU. Software support (ROCm, vLLM, llama.cpp) is documented but doesn't substitute for the missing capacity/bandwidth evidence needed to judge practicality for 70B inference.", "evidenceIds": [ "amd-rx-9070-xt-docs-10", "amd-rx-9070-xt-docs-11", "amd-rx-9070-xt-docs-12" ] }, { "productId": "amd-rx-9070-xt", "storyId": "tensor-throughput-disclosed", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers ROCm software support, framework compatibility, and general features, but contains no published TFLOPS/TOPS figures for the RX 9070 XT with precision (FP16/FP32/INT8) or sparsity stated. Missing for 10: any tensor throughput numbers, precision breakdown, sparsity conditions, or comparison to a baseline needed for training/inference sizing.", "evidenceIds": [ "amd-rx-9070-xt-docs-7", "amd-rx-9070-xt-docs-8" ] }, { "productId": "amd-rx-9070-xt", "storyId": "upscaling-frame-generation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "AMD's product page confirms FSR (\"Redstone\") support on the RX 9070 XT, and third-party review corroborates strong raster/ray-tracing performance, but the evidence pack lacks detail on frame-generation specifics or a documented list of supported games. missing for 10: explicit frame-generation (FSR 3/4) feature naming, game compatibility list/count, independent hands-on upscaling benchmarks.", "evidenceIds": [ "amd-rx-9070-xt-docs-15", "amd-rx-9070-xt-comm-1" ] }, { "productId": "nvidia-b200", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "A probe confirms docs.nvidia.com serves an llms.txt file explicitly described as a collection for AI user agents, and product docs pages are also available in agent-friendly markdown form (.md variant returns 200). This directly demonstrates agent-oriented documentation exists and is reachable. Missing for 10: no evidence of an official announcement/first-party documentation explaining or promoting the llms.txt initiative, and no independent/community confirmation of agents actually consuming it.", "evidenceIds": [ "nvidia-b200-probe-1", "nvidia-b200-probe-2" ] }, { "productId": "nvidia-b200", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The B200 is a hardware GPU/data-center platform, not an agentic automation or workflow orchestration tool; setting up autonomous background automations is a software/agent-layer capability that runs on top of hardware like this, not something the GPU product itself provides.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-headless", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "DGX B200 docs show strong headless-operation building blocks — CLI tools like nvidia-smi, Docker Engine/NVIDIA Container Toolkit for containerized workloads, and remote management via Redfish/IPMI/SNMP — all of which support running the system without a GUI and integrating into automated pipelines. However, there is no explicit mention of CI/CD integration, scripting examples, or automation-specific tooling (e.g., APIs for job orchestration in CI systems). Missing for 10: explicit CI/CD pipeline integration docs, automation API examples, and independent confirmation of headless CI usage in production.", "evidenceIds": [ "nvidia-b200-docs-3", "nvidia-b200-docs-4", "nvidia-b200-docs-6", "nvidia-b200-docs-9", "nvidia-b200-docs-10" ] }, { "productId": "nvidia-b200", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The B200 is a GPU hardware product for data centers, not an AI agent or application layer that could plug in MCP servers to use tools; this axis is a category error for hardware.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The B200 is a GPU hardware product for AI compute infrastructure, not an agent platform or service that would expose an MCP server for agent connectivity; this axis is a category error for a hardware accelerator.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "DGX B200 exposes machine-manageable interfaces (Redfish, IPMI, SNMP, KVM) and CLI tooling like nvidia-smi for GPU/system state, which could be scripted by an AI-native agent, but this is BMC/system management, not a documented public API for driving AI workloads or product capabilities, and a direct openapi.json probe returned 404 on all candidate paths, indicating no formal API spec is published. missing for 10: a documented REST/gRPC API with schema (OpenAPI/Swagger) for programmatic control of the AI workload itself, SDK or client library documentation, and independent evidence of agents driving it via API.", "evidenceIds": [ "nvidia-b200-docs-10", "nvidia-b200-docs-6", "nvidia-b200-probe-3" ] }, { "productId": "nvidia-b200", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU/system product, not an API/service platform that issues credentials for agents; scoped API credential management is outside its product category.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "agentic-sdks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers DGX B200 hardware, system administration, health monitoring, and container tooling (Docker/NVIDIA Container Toolkit), but contains no documentation of official SDKs (e.g., CUDA, cuDNN, TensorRT, NIM APIs) that AI-native developers would build against. Building against SDKs is a fair and applicable axis for an NVIDIA AI hardware platform, but nothing in this pack demonstrates it.", "evidenceIds": [ "nvidia-b200-docs-9", "nvidia-b200-docs-4" ] }, { "productId": "nvidia-b200", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU/system product, not a service or platform with an event-driven API; webhook subscriptions are a category mismatch for this product type.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "api-interactive-docs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU product; an interactive API reference with runnable examples is a developer-portal/SDK feature axis, not applicable to the physical hardware itself.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The DGX B200 docs mention management protocols like Redfish/IPMI/SNMP but no machine-readable API spec (OpenAPI or equivalent) is provided; a direct probe for OpenAPI/swagger files at NVIDIA's docs domain returned 404 for all candidate paths, confirming no such spec is published.", "evidenceIds": [ "nvidia-b200-docs-10", "nvidia-b200-probe-3" ] }, { "productId": "nvidia-b200", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a GPU hardware/data-center product, not an application or SaaS with a sandbox/production data separation concept; sandbox-vs-production testing is not a fair axis for this kind of product.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The B200 is a hardware GPU/system product, not an API-driven software service; versioned APIs with deprecation policies are not a relevant axis for this kind of product.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU/system product; defining event-triggered automation rules is an application/orchestration-layer concern, not a fair axis for a GPU hardware platform.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU/data-center system, not an automation-building or workflow tool; versioning, reviewing, and rolling back 'automations' is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "compute-toolchain-support", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Evidence confirms B200 systems ship with NVIDIA Container Toolkit/Docker and standard NVIDIA tooling (nvidia-smi) plus community confirmation the hardware works well for compute workloads, implying CUDA support as the standard NVIDIA stack. However, no direct evidence cites CUDA toolkit version/compatibility docs explicitly listing B200 as a supported compute target, and community notes real friction around GPU virtualization/isolation on this hardware. Missing for 10: explicit CUDA toolkit release notes naming B200 as supported target, and clearer resolution of virtualization/isolation caveats.", "evidenceIds": [ "nvidia-b200-docs-9", "nvidia-b200-docs-6", "nvidia-b200-comm-1", "nvidia-b200-comm-3", "nvidia-b200-comm-4" ] }, { "productId": "nvidia-b200", "storyId": "creator-media-acceleration", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "datacenter-scale-out", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "NVIDIA documents DGX B200 multi-GPU systems, DGX SuperPOD rack-scale deployment, cluster/data-center management software, and Mission Control for AI factory operations, with community confirmation that B200 systems deliver strong performance gains over prior generation. Missing for 10: explicit NVLink/Infinity Fabric bandwidth specs in this evidence pack and independent large-scale training/serving benchmarks beyond community sentiment.", "evidenceIds": [ "nvidia-b200-docs-1", "nvidia-b200-docs-4", "nvidia-b200-docs-11", "nvidia-b200-docs-12", "nvidia-b200-comm-1", "nvidia-b200-comm-3" ] }, { "productId": "nvidia-b200", "storyId": "efficient-sustained-workloads", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence covers performance claims, PSU redundancy, and management tools but there is no documented power envelope specification with independent performance-per-watt testing for sustained workloads. missing for 10: TDP/power envelope specs, independent perf-per-watt benchmarks, sustained workload efficiency data.", "evidenceIds": [ "nvidia-b200-docs-1", "nvidia-b200-docs-5", "nvidia-b200-docs-6" ] }, { "productId": "nvidia-b200", "storyId": "high-refresh-4k-gaming", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "linux-first-class", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "DGX B200 docs assume a Linux-based administration stack (nvidia-smi, Docker Engine/NVIDIA Container Toolkit, command-line health checks, BMC/Redfish), and community posts describe hands-on Linux work (vGPU/NVLink virtualization, Cloud Hypervisor driver reverse-engineering) confirming real independent Linux usage and testing. However, there is no explicit Linux driver release-notes page, versioned driver changelog, or formal independent Linux benchmark/validation report in the evidence. Missing for 10: dedicated Linux driver release documentation/changelog, formal independent Linux benchmark reports validating driver quality.", "evidenceIds": [ "nvidia-b200-docs-3", "nvidia-b200-docs-6", "nvidia-b200-docs-9", "nvidia-b200-comm-3", "nvidia-b200-comm-5" ] }, { "productId": "nvidia-b200", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Evidence confirms B200 inference performance claims and mentions vLLM in a community context (KV cache offload), and NVLink/GPU virtualization discussions imply serving-stack usage, but there is no explicit documentation of FP8/FP4 precision support or named serving stack compatibility (TensorRT-LLM, vLLM, ROCm, llama.cpp) tied specifically to B200. missing for 10: explicit FP8/FP4 precision docs, explicit TensorRT-LLM/vLLM/llama.cpp support statements, ROCm compatibility (irrelevant for NVIDIA but story implies breadth), independent benchmarks confirming serving stack performance.", "evidenceIds": [ "nvidia-b200-docs-1", "nvidia-b200-comm-4", "nvidia-b200-comm-3" ] }, { "productId": "nvidia-b200", "storyId": "memory-spec-bandwidth", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no specific memory capacity, memory type, bus width, or bandwidth figures for the B200 — only general marketing claims, DGX system admin docs, and community commentary about virtualization difficulties, none of which state the actual memory spec numbers.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "ml-framework-support", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Evidence confirms DGX B200 ships with Docker/NVIDIA Container Toolkit and general AI workflow acceleration claims, and community posts confirm real-world usage of B200 for ML workloads, but there is no explicit citation of official PyTorch/TensorFlow build documentation, CUDA/cuDNN version support matrices, or framework compatibility tables. missing for 10: explicit PyTorch/framework support matrix documentation, CUDA/cuDNN version compatibility details, official framework install/build instructions for B200.", "evidenceIds": [ "nvidia-b200-docs-9", "nvidia-b200-docs-2", "nvidia-b200-comm-1", "nvidia-b200-comm-2" ] }, { "productId": "nvidia-b200", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of open-source kernel modules or upstream Linux driver support for B200; documentation only references proprietary NVIDIA tooling (nvidia-smi, Container Toolkit, BMC/Redfish) with no mention of open-driver support.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU/system product, not a UI+API software product; the notion of 'API parity with UI' is a category error for a physical accelerator platform (though it exposes CLI/BMC tools like nvidia-smi and Redfish, these are not a UI/API pair in the sense this story asks about).", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a GPU hardware product for compute infrastructure, not a data platform or SaaS that stores user data subject to export/portability concerns; data export/open-format portability is not a fair axis for a GPU accelerator.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "openness-self-host", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "The DGX B200 is physical hardware/on-prem infrastructure explicitly designed to be deployed and administered in a customer's own data center, with first-party docs covering node administration, health monitoring, power/cooling redundancy, BMC/Redfish/IPMI management, and container tooling for running workloads locally — all consistent with self-hosting the core product. Missing for 10: independent third-party accounts of a full self-hosting deployment lifecycle (procurement to production) and clearer distinction from cloud-rental usage mentioned in community comments.", "evidenceIds": [ "nvidia-b200-docs-3", "nvidia-b200-docs-4", "nvidia-b200-docs-5", "nvidia-b200-docs-9", "nvidia-b200-docs-10", "nvidia-b200-docs-14" ] }, { "productId": "nvidia-b200", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a GPU hardware/accelerator product, not a hosted data service; data residency/region selection is a SaaS/cloud-service concern that depends on where an operator deploys the hardware, not a capability of the chip or DGX system itself.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The B200 is a hardware GPU/system product, not a data service or SaaS platform that stores user data; data retention/deletion policies are an axis for software/data services, not for a hardware accelerator itself.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "NVIDIA B200 is a hardware GPU/system product, not a software service with telemetry/usage tracking to opt out of; this privacy-posture axis about opting out of vendor telemetry does not apply to this category.", "evidenceIds": [] }, { "productId": "nvidia-b200", "storyId": "run-70b-local-llm", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains no published VRAM capacity or memory bandwidth specs for B200, nor any concrete claim about running 70B-class quantized models; docs cover DGX system administration, power, networking and support rather than memory specs, and community comments are generic ('B200 is better than H200') or about vGPU/memory-offload complications rather than confirming single-node 70B inference capacity. missing for 10: published HBM VRAM capacity figure, published memory bandwidth figure, explicit 70B-quantized-model inference benchmark or claim.", "evidenceIds": [ "nvidia-b200-docs-1", "nvidia-b200-comm-1", "nvidia-b200-comm-4" ] }, { "productId": "nvidia-b200", "storyId": "tensor-throughput-disclosed", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains only relative performance claims (3X training, 15X inference vs prior gen) and general DGX platform/management docs, but no actual published TFLOPS/TOPS numbers with precision (FP8, FP16, INT8, etc.) or sparsity conditions stated for B200. Without these hard spec figures, an ML engineer cannot size workloads from the evidence provided.", "evidenceIds": [ "nvidia-b200-docs-1" ] }, { "productId": "nvidia-b200", "storyId": "upscaling-frame-generation", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-agent-docs", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "A probe confirms developer.nvidia.com/llms.txt returns HTTP 200 with a real summary, showing NVIDIA does expose an agent-readable entry point for its developer docs, but this is a generic portal file, not specific to H200 GPU documentation, and other agent-friendly formats (docs.md, OpenAPI) return 404s. Missing for 10: H200-specific machine-readable docs, working docs.md/OpenAPI endpoints, and confirmation an agent can navigate beyond the root llms.txt.", "evidenceIds": [ "nvidia-h200-sxm-probe-1", "nvidia-h200-sxm-probe-2", "nvidia-h200-sxm-probe-3" ] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU accelerator, not an automation/orchestration platform; setting up autonomous background automations is an application-layer/software capability entirely outside the scope of a data-center GPU product.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-headless", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU accelerator, not an AI agent or assistant capable of plugging in MCP servers to use their tools; this axis is a category error for a physical compute product.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU product, not an agent or software platform that could expose an MCP server; connecting agents via MCP is a wrong axis for a datacenter accelerator.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-public-api", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The H200 is a hardware accelerator; while CUDA Toolkit and Nsight tools provide programming interfaces, there is no evidence of a documented public REST/agentic API for driving the product, and explicit probes for OpenAPI/swagger specs all returned 404s.", "evidenceIds": [ "nvidia-h200-sxm-probe-2", "nvidia-h200-sxm-probe-3" ] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "H200 is a hardware GPU product, not an API/service platform issuing credentials for agents; scoped API credential issuance is a wrong axis for a hardware accelerator.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-sdks", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "NVIDIA provides official SDKs to build against (CUDA Toolkit, Nsight developer tools, and NIM microservices bundled via NVIDIA AI Enterprise) that target the H200 hardware, and a developer portal exists with an llms.txt discovery file. However, probes show no machine-readable docs (404 on .md) and no OpenAPI/swagger spec, and there's no H200-specific agentic SDK evidence beyond generic CUDA/NIM tooling. missing for 10: agent-specific SDK examples, machine-readable API docs (docs-md returned 404), OpenAPI spec availability, independent developer corroboration of SDK usability for AI-native/agentic workflows.", "evidenceIds": [ "nvidia-h200-sxm-docs-9", "nvidia-h200-sxm-docs-10", "nvidia-h200-sxm-docs-11", "nvidia-h200-sxm-probe-1", "nvidia-h200-sxm-probe-2", "nvidia-h200-sxm-probe-3" ] }, { "productId": "nvidia-h200-sxm", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU product; webhooks/event subscriptions are a software/platform API concept that does not apply to a physical accelerator SKU.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "api-interactive-docs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU product, not an API/SaaS service; an interactive API reference with runnable examples is not a fair axis for a physical accelerator (this differs from CUDA toolkit docs, which are a separate developer tool, not the GPU itself).", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU; NVIDIA's developer portal was probed for machine-readable API specs (openapi.json, swagger.json, etc.) and all returned 404, showing no discoverable OpenAPI spec is published.", "evidenceIds": [ "nvidia-h200-sxm-probe-3", "nvidia-h200-sxm-probe-2" ] }, { "productId": "nvidia-h200-sxm", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU product; sandbox-vs-production data testing is an application/software-layer concern, not a fair axis for a GPU accelerator itself.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU product, not a software service with a versioned API; no such API/deprecation-policy axis applies to a physical accelerator card.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU/accelerator; rule-based event-triggered automation is a software/application-layer feature entirely outside the scope of a physical GPU product's capabilities.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU product; versioning, reviewing, and rolling back automations is a software/workflow-orchestration concern entirely outside a hardware accelerator's scope.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "compute-toolchain-support", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "CUDA Toolkit is NVIDIA's standard GPU-compute toolchain, explicitly documented as supporting deployment across data centers and supercomputers, and H200 is the current flagship data-center GPU in this same product family/lineage; community evidence (production LLM inference deployments on H200 SXM) confirms real-world CUDA-based workloads running on this part. Missing for 10: no explicit CUDA compute-capability/architecture list page directly naming 'H200' as a supported gpu-architecture target string, and no ROCm angle (not applicable to NVIDIA anyway).", "evidenceIds": [ "nvidia-h200-sxm-docs-10", "nvidia-h200-sxm-docs-11", "nvidia-h200-sxm-comm-1", "nvidia-h200-sxm-probe-rt-1" ] }, { "productId": "nvidia-h200-sxm", "storyId": "creator-media-acceleration", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "datacenter-scale-out", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Evidence confirms the SXM form factor, MIG partitioning, CUDA toolkit for datacenter deployment, and community proof of real multi-GPU serving (Llama 405B at 142 tok/s across H200 SXMs) validating datacenter-scale operation, but the pack never cites NVLink/NVSwitch bandwidth figures, Infinity Fabric, or DGX/HGX rack-scale system specs that the story explicitly asks for. missing for 10: explicit NVLink/NVSwitch bandwidth numbers, DGX/HGX rack-scale system documentation, independent rack-scale benchmark corroboration.", "evidenceIds": [ "nvidia-h200-sxm-docs-2", "nvidia-h200-sxm-docs-5", "nvidia-h200-sxm-comm-1", "nvidia-h200-sxm-docs-10" ] }, { "productId": "nvidia-h200-sxm", "storyId": "efficient-sustained-workloads", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "NVIDIA's own docs state the H200 operates 'within the same power profile as the H100' (nvidia-h200-sxm-docs-4), but this is a vendor claim only — no independent performance-per-watt benchmarks or third-party power-envelope testing are present in the evidence pack. Community notes (nvidia-h200-sxm-comm-2) even highlight that H200 shares H100 silicon, which is consistent but not an efficiency benchmark. Missing for 10: independent/hands-on power-draw measurements under sustained load, third-party perf/watt comparisons, and detailed thermal/power documentation beyond the single marketing sentence.", "evidenceIds": [ "nvidia-h200-sxm-docs-4", "nvidia-h200-sxm-comm-2" ] }, { "productId": "nvidia-h200-sxm", "storyId": "high-refresh-4k-gaming", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "linux-first-class", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence pack lacks any explicit mention of Linux driver releases, release notes, or independent Linux benchmarking/testing of the H200; CUDA toolkit blurb only vaguely references 'data centers' and 'supercomputers' without naming Linux, and community links focus on inference throughput or die comparisons, not OS-specific testing.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "NVIDIA docs and runtime probes confirm H200's HBM3e specs and FP8 tensor performance figures, and community evidence shows real-world LLM inference (Llama2, Llama 405B) throughput gains, but no evidence names FP4 support or specific serving-stack integration (TensorRT-LLM, vLLM, ROCm, llama.cpp) for this part. missing for 10: explicit FP4 precision docs, named serving-stack support (TensorRT-LLM/vLLM/ROCm/llama.cpp) tied to H200.", "evidenceIds": [ "nvidia-h200-sxm-docs-1", "nvidia-h200-sxm-docs-2", "nvidia-h200-sxm-comm-1", "nvidia-h200-sxm-probe-rt-1" ] }, { "productId": "nvidia-h200-sxm", "storyId": "memory-spec-bandwidth", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "NVIDIA's official H200 page publishes capacity (141GB), memory type (HBM3e), and bandwidth (4.8TB/s), and a runtime probe confirms this spec table is live and fetchable; independent commentary also confirms it's HBM3e stacks on the H100 die. However, memory bus width is never stated anywhere in the evidence pack. Missing for 10: explicit memory bus-width figure, and any independent/third-party spec-sheet corroboration beyond NVIDIA's own page.", "evidenceIds": [ "nvidia-h200-sxm-docs-2", "nvidia-h200-sxm-probe-rt-1", "nvidia-h200-sxm-comm-2" ] }, { "productId": "nvidia-h200-sxm", "storyId": "ml-framework-support", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "NVIDIA documents CUDA Toolkit support for developing/deploying GPU-accelerated applications and community evidence (HN inference benchmark) shows PyTorch-based LLM workloads (Llama 405B) running in production on H200 SXM, implying framework compatibility via CUDA. However, there is no direct citation of an official PyTorch/TensorFlow support matrix or explicit framework-version compatibility documentation for H200 specifically. Missing for 10: explicit PyTorch/TensorFlow official support matrix naming H200, CUDA/cuDNN version compatibility table, first-party framework installation guide referencing H200.", "evidenceIds": [ "nvidia-h200-sxm-docs-10", "nvidia-h200-sxm-docs-11", "nvidia-h200-sxm-comm-1" ] }, { "productId": "nvidia-h200-sxm", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows only proprietary CUDA toolkit and NVIDIA AI Enterprise stack; nothing about open-source kernel modules (nvidia-open) or upstream Linux driver support is documented in the pack. Missing for 10: any mention of NVIDIA's open GPU kernel modules, upstream mainline Linux driver support, or open-source driver documentation for the H200.", "evidenceIds": [ "nvidia-h200-sxm-docs-10", "nvidia-h200-sxm-docs-11" ] }, { "productId": "nvidia-h200-sxm", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a physical GPU/hardware product with no user-facing UI or API of its own — it's accessed via drivers, CUDA, and third-party platforms. The 'API vs UI parity' story is a category error for a hardware accelerator.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU accelerator, not a data platform or SaaS application that stores user data subject to export/lock-in concerns; data portability/exit is not an applicable axis for a compute chip.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "openness-self-host", "verdict": "full", "quality": 7, "confidence": "high", "rationale": "The H200 is physical hardware you purchase/own and deploy in your own datacenter or colo, making self-hosting the core product inherently possible (unlike SaaS AI products), and NVIDIA's stack (CUDA toolkit, drivers, Nsight tools) supports fully on-prem deployment across data centers and workstations. missing for 10: no direct documentation of an on-prem purchase/procurement path or hands-on self-hosting case study, and no independent report confirming ease of self-managed deployment outside of cloud providers.", "evidenceIds": [ "nvidia-h200-sxm-docs-10", "nvidia-h200-sxm-docs-11", "nvidia-h200-sxm-docs-2", "nvidia-h200-sxm-comm-1" ] }, { "productId": "nvidia-h200-sxm", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU/chip, not a hosted data storage or cloud service; data residency/region selection is a deployment-layer concern determined by whoever operates the data center, not a property of the GPU itself. This axis is a category error for a hardware product.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU accelerator, not a data service or platform that stores/retains user data; data retention/deletion controls are a SaaS/application-layer concern handled by whatever software stack runs on top of the GPU, not by the chip itself.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The H200 is a hardware GPU accelerator, not a software service or agent that collects usage telemetry from end users; telemetry opt-out is not a meaningful axis for a physical chip product.", "evidenceIds": [] }, { "productId": "nvidia-h200-sxm", "storyId": "run-70b-local-llm", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "NVIDIA docs confirm 141GB HBM3e at 4.8TB/s, comfortably fitting a 70B-class quantized model with large batch/context headroom, and this is corroborated by an independent runtime probe and community benchmarks showing real-world inference (e.g., Llama 405B at 142 tok/s) on H200 SXM. Missing for 10: independent hands-on benchmark specifically for a 70B-class quantized model rather than larger models.", "evidenceIds": [ "nvidia-h200-sxm-docs-2", "nvidia-h200-sxm-comm-1", "nvidia-h200-sxm-probe-rt-1" ] }, { "productId": "nvidia-h200-sxm", "storyId": "tensor-throughput-disclosed", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA's H200 page is confirmed live and does contain a spec table with FP8 tensor figures and bandwidth/capacity numbers (per probe-rt-1), but the evidence pack itself surfaces mostly bare marketing multipliers ('2X faster than H100', '110X faster than CPU') rather than the actual precision-tagged TFLOPS/TOPS figures with sparsity conditions spelled out. Community commentary also notes the H200 reuses H100 silicon, adding skepticism to headline comparisons rather than to the raw spec numbers themselves. Missing for 10: explicit quoted TFLOPS/TOPS values per precision (FP8/FP16/INT8) with dense vs. sparse figures, and independent benchmark corroboration of those specific numbers.", "evidenceIds": [ "nvidia-h200-sxm-docs-1", "nvidia-h200-sxm-docs-2", "nvidia-h200-sxm-probe-rt-1", "nvidia-h200-sxm-comm-2" ] }, { "productId": "nvidia-h200-sxm", "storyId": "upscaling-frame-generation", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "A probe confirms that NVIDIA's developer portal serves a working llms.txt file (HTTP 200) with descriptive content about NVIDIA's accelerated computing and AI ecosystem, which an AI agent could be pointed at. Missing for 10: independent/community confirmation of agent usage, deeper content excerpt showing structured agent-oriented guidance, and consistency across other doc endpoints (docs-md and openapi both 404).", "evidenceIds": [ "nvidia-rtx-5070-ti-probe-1" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA's Project G-Assist is described as an AI assistant that helps tune, control, and optimize the system based on its data, which loosely matches 'AI-generated insights from my data,' but this is a narrow system-optimization feature rather than a general data-insights capability, and it runs via software on top of the GPU rather than being a core product feature. Missing for 10: evidence of broader data analysis/insights generation beyond system tuning, first-party detail on G-Assist's actual insight outputs, and independent/hands-on corroboration that it delivers meaningful 'insights and suggestions' from user data.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-10", "nvidia-rtx-5070-ti-docs-17" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5070 Ti is a consumer GPU hardware product, not an automation/agent platform; setting up background autonomous automations is a software/orchestration capability outside a GPU's product category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "NVIDIA Project G-Assist is documented as a built-in AI assistant on GeForce RTX PCs that can 'tune, control, and optimize your system,' which is a form of task delegation, but it's scoped narrowly to system/GPU settings rather than general-purpose task delegation. Missing for 10: independent/hands-on corroboration of G-Assist's capabilities, detail on the scope of tasks it can perform, and confirmation it ships broadly rather than as a limited beta feature.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-10" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-headless", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU is hardware; MCP server integration is an agent/software-tooling concept that does not apply to a graphics card product category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product is not an agentic software platform that could plausibly ship an official MCP server; this axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-public-api", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product has no concept of API credentialing or scoped access control for agents; this axis is a category error for a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-sdks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "NVIDIA provides official developer SDKs (CUDA Toolkit, CUDA Tile C++, cuTile Python, Nsight Compute/Systems) and curated AI SDKs/backends (PyTorch, Ollama, Windows ML) that developers can build against on RTX GPUs, backed by a live developer portal. Missing for 10: independent hands-on developer corroboration, deeper API reference documentation, and agent-specific SDK examples beyond general CUDA/AI tooling.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-17", "nvidia-rtx-5070-ti-docs-18", "nvidia-rtx-5070-ti-docs-19", "nvidia-rtx-5070-ti-docs-20", "nvidia-rtx-5070-ti-probe-1" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product has no concept of webhook event subscriptions; this axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "api-interactive-docs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a consumer GPU hardware product, not an API/SDK service with a developer reference; while CUDA docs exist tangentially, an interactive runnable API reference is not a fair expectation of a graphics card product itself.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "api-machine-spec", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5070 Ti is a consumer GPU hardware product, not a service or platform with an API; a machine-readable API spec is not a fair expectation for a graphics card itself. Probe results confirm no OpenAPI spec exists, but this is a category mismatch rather than a missing feature.", "evidenceIds": [ "nvidia-rtx-5070-ti-probe-3" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product is not a service or platform that provides sandboxed testing environments distinct from production data; this axis is a category error for a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5070 Ti is a consumer GPU hardware product, not an API/service platform; versioned APIs with deprecation policies is a category error for this axis.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product is not the kind of thing that defines automation rules/triggers for events; this is a software/platform-level capability, not a graphics card axis.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This story concerns versioning/reviewing/rolling back automations, which is a software workflow/tooling capability irrelevant to a GPU hardware product; the evidence pack covers graphics, AI rendering, and hardware features with no automation-versioning concept.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "compute-toolchain-support", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "The evidence pack shows NVIDIA's CUDA toolkit developer portal and tools (CUDA Tile C++, cuTile Python, Nsight Compute/Systems) exist and are actively documented, confirming CUDA as NVIDIA's GPU-compute toolchain. However, none of the citations explicitly name the RTX 5070 Ti or Blackwell architecture as a supported CUDA compute target, and a probe for the CUDA toolkit docs page returned 404, weakening direct confirmation. missing for 10: explicit RTX 5070 Ti/Blackwell CUDA compute-capability listing, architecture-specific SDK/driver support notes, independent developer confirmation of compute workloads running on this card.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-18", "nvidia-rtx-5070-ti-docs-19", "nvidia-rtx-5070-ti-docs-20", "nvidia-rtx-5070-ti-probe-2" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "creator-media-acceleration", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "NVIDIA's own product page documents creator-relevant acceleration: 9th-gen NVENC encoder usage in DaVinci Resolve/Adobe Premiere (doc-15), broad video editing/3D rendering/graphic design acceleration (doc-12), RTX Video Super Resolution/HDR (doc-14), and AI background noise removal (doc-13). However, the evidence never names AV1/HEVC codec specifics for the encoder, nor cites any formal ISV certification (e.g., Studio Driver certification for specific creative apps), and there is no independent/hands-on corroboration of creator-app acceleration claims. Missing for 10: explicit AV1/HEVC encode/decode spec documentation, named ISV/professional driver certification program details, and third-party validation of creator workflow performance.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-15", "nvidia-rtx-5070-ti-docs-12", "nvidia-rtx-5070-ti-docs-14", "nvidia-rtx-5070-ti-docs-13" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "datacenter-scale-out", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The GeForce RTX 5070 Ti is a consumer gaming GPU with no NVLink support (NVLink was dropped from GeForce cards after the 30-series), no Infinity Fabric (an AMD interconnect, not NVIDIA), and no documented rack-scale/multi-GPU datacenter deployment story. Evidence only covers gaming features (DLSS, Reflex), local AI inference tools (ComfyUI, Ollama), and CUDA/Nsight developer tools—none address datacenter-scale interconnect or multi-GPU rack deployment.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-17", "nvidia-rtx-5070-ti-docs-18", "nvidia-rtx-5070-ti-docs-19", "nvidia-rtx-5070-ti-docs-20" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "efficient-sustained-workloads", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence covers DLSS, cooling design, and CUDA tooling, but there is no documented TDP/power envelope specification and no independent performance-per-watt testing for the RTX 5070 Ti under sustained ML workloads. One community review mentions cooling noise/temperature but not power efficiency metrics or perf/watt benchmarking. Missing for 10: documented power envelope specs, independent sustained-workload power draw measurements, performance-per-watt benchmarks for ML/AI workloads.", "evidenceIds": [ "nvidia-rtx-5070-ti-comm-1" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "high-refresh-4k-gaming", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA's own docs describe DLSS 4/Multi Frame Generation, path tracing, and Reflex as performance features that would enable high-refresh 4K play, and a TechPowerUp review confirms the card exists and runs cool/quiet, but no cited evidence gives actual independent 4K/high-refresh FPS benchmark numbers corroborating the vendor's performance claims. Missing for 10: independent game benchmark FPS/refresh-rate data at 4K, third-party comparison against vendor performance charts.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-1", "nvidia-rtx-5070-ti-docs-2", "nvidia-rtx-5070-ti-docs-7", "nvidia-rtx-5070-ti-docs-8", "nvidia-rtx-5070-ti-comm-1" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "linux-first-class", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions Linux drivers, Linux support documentation, or independent Linux benchmarks/testing for the RTX 5070 Ti; all evidence is Windows-centric feature marketing, CUDA toolkit docs, or general reviews.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "The evidence pack is almost entirely gaming/creator-focused (DLSS, Reflex, NVENC) with only one line noting RTX AI PCs 'maximize performance across Windows ML, Ollama, PyTorch, and other inference backends' — a thin nod to LLM inference support but without naming TensorRT-LLM, vLLM, or llama.cpp, and no mention of FP8/FP4 precision support for this specific GPU. missing for 10: explicit FP8/FP4 quantization documentation, named support for TensorRT-LLM/vLLM/llama.cpp, and any benchmark or hands-on inference throughput data for the 5070 Ti specifically.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-17" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "memory-spec-bandwidth", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no official spec sheet listing VRAM capacity, memory type, bus width, or bandwidth for the RTX 5070 Ti; only marketing/DLSS content and vague community chatter about GDDR6X speeds on a different card are present.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "ml-framework-support", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "NVIDIA's own RTX 50-series page explicitly lists PyTorch as one of the supported inference backends optimized for RTX AI PCs, and the CUDA Toolkit page (which underlies PyTorch's GPU support) is referenced with developer tooling (Nsight, CUDA Tile). However, there is no cited official PyTorch build/support matrix, compute-capability compatibility table, or version-pinned install instructions specific to the 5070 Ti/Blackwell architecture. missing for 10: explicit PyTorch official support matrix/compute-capability listing, versioned install docs confirming Blackwell (sm_120) support, independent hands-on confirmation of PyTorch running on this GPU.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-17", "nvidia-rtx-5070-ti-docs-18", "nvidia-rtx-5070-ti-docs-19", "nvidia-rtx-5070-ti-docs-20" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "NVIDIA proprietary driver is well known to be closed-source (only kernel module wrapper is partially open for older architectures), but the evidence pack contains no mention of open-source kernel modules, upstream Linux kernel driver support, or vendor documentation of open driver support for the RTX 5070 Ti/Blackwell architecture. All evidence focuses on DLSS, CUDA, and consumer software features, none addressing driver-openness.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5070 Ti is a consumer GPU hardware product, not a software product with a UI/API duality; this API-vs-UI parity axis is a category error for a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5070 Ti is a consumer GPU hardware product, not a data-storing SaaS/platform; there is no concept of user account data to export or a lock-in relationship to exit from. Data export/portability is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains no published TDP/TGP figures, power-connector (e.g., 12V-2x6) requirements, recommended PSU wattage, or official cooling/thermal guidance for the RTX 5070 Ti — only marketing content about DLSS/AI features and a single community review noting one AIB card's cooler ran cool/quiet. missing for 10: official TDP/TGP spec, connector type and PSU wattage recommendation, reference cooling/thermal design guidance, any first-party spec sheet citation.", "evidenceIds": [ "nvidia-rtx-5070-ti-comm-1" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a consumer GPU hardware product, not a cloud/SaaS data-storage service; data residency/region selection is not a fair axis for a physical GPU that runs locally on a user's own PC.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "privacy-no-training", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA markets local AI inference on RTX GPUs as 'fully private on your RTX-powered PC' (doc-9), implying that running models locally avoids sending data to cloud services where it could be used for training. However, there is no explicit privacy policy, opt-out mechanism, or documented commitment about data-training use — missing for 10: explicit data-usage/training policy, opt-out controls, and independent verification of the 'fully private' claim.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-9", "nvidia-rtx-5070-ti-docs-11", "nvidia-rtx-5070-ti-docs-17" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a hardware GPU product; data retention/deletion controls are a data-processing/service policy concern, not applicable to a physical graphics card's axis.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product is not a service with account-level telemetry/usage tracking controls in the sense this story implies; this axis is a category error for a graphics card SKU rather than a software/SaaS product.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "run-70b-local-llm", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only shows generic AI/DLSS marketing and confirms a 16GB VRAM capacity (via the community GDDR6X comparison), well short of what's needed to run a 70B-class quantized LLM locally; no vendor documentation specifies VRAM/bandwidth sufficient for such workloads. missing for 10: explicit VRAM/bandwidth specs, any benchmark or claim of running 70B-parameter models, quantization guidance for large LLMs on this card.", "evidenceIds": [ "nvidia-rtx-5070-ti-comm-2", "nvidia-rtx-5070-ti-docs-17", "nvidia-rtx-5070-ti-docs-9" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "tensor-throughput-disclosed", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains only marketing copy about DLSS, RTX features, and general AI PC messaging, with no published TFLOPS/TOPS figures broken down by precision (FP16/FP8/INT8) or sparsity for the RTX 5070 Ti — exactly the bare-marketing-number problem the story warns against.", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-9", "nvidia-rtx-5070-ti-docs-17", "nvidia-rtx-5070-ti-comm-2" ] }, { "productId": "nvidia-rtx-5070-ti", "storyId": "upscaling-frame-generation", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "NVIDIA's official documentation confirms DLSS Multi Frame Generation, Super Resolution, Ray Reconstruction, and DLAA are supported on the RTX 5070 Ti, with the NVIDIA app enabling updates across hundreds of supported games. Independent community reviews corroborate the card's real-world performance and existence of these features in practice. Missing for 10: an explicit third-party benchmark showing DLSS/FG frame-rate gains in specific titles, and no mention of FSR support (AMD tech, not expected on NVIDIA cards).", "evidenceIds": [ "nvidia-rtx-5070-ti-docs-1", "nvidia-rtx-5070-ti-docs-3", "nvidia-rtx-5070-ti-docs-4", "nvidia-rtx-5070-ti-docs-5", "nvidia-rtx-5070-ti-docs-6", "nvidia-rtx-5070-ti-comm-1" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-agent-docs", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "A probe confirms developer.nvidia.com/llms.txt returns HTTP 200 with a descriptive summary of NVIDIA's developer portal, showing agent-oriented docs discovery is possible. However, this is for the general NVIDIA Developer ecosystem rather than RTX 5080-specific docs, and related agent-friendly formats (markdown docs, OpenAPI spec) return 404s. Missing for 10: RTX 5080-specific llms.txt/agent docs, working markdown doc mirrors, and an accessible OpenAPI/machine-readable spec.", "evidenceIds": [ "nvidia-rtx-5080-probe-1", "nvidia-rtx-5080-probe-2", "nvidia-rtx-5080-probe-3" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA's Project G-Assist is described as an AI assistant that helps tune, control, and optimize the system based on the user's PC configuration, which loosely maps to 'AI-generated insights from my data,' but it is a narrow system-tuning helper rather than a broader data-insight/agentic feature. Missing for 10: detailed documentation of G-Assist's data sources/insight generation, independent hands-on validation, and any indication it works beyond basic system optimization suggestions.", "evidenceIds": [ "nvidia-rtx-5080-docs-12" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX 5080 is a GPU hardware product, not an automation/agent platform; the concept of setting up autonomous background automations is a category error for this axis—it's a wrong axis for a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "NVIDIA documents 'Project G-Assist,' a built-in AI assistant on GeForce RTX PCs that can tune, control, and optimize system settings — directly matching the delegate-tasks story. However, it's labeled a 'Project' (experimental/beta), with no independent hands-on corroboration of its task-delegation capabilities or scope beyond system tuning. Missing for 10: independent/hands-on verification of G-Assist's task range and reliability, clarity on general-availability status beyond beta.", "evidenceIds": [ "nvidia-rtx-5080-docs-12" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-headless", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5080 is a graphics card/hardware product, not an AI agent or software platform that could plug in MCP servers to use their tools; this axis is a category error for a GPU.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX 5080 is a consumer GPU hardware product, not an agent platform or service that could plausibly ship an MCP server for agent connectivity; this axis is a category error for a GPU.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA Project G-Assist is documented as an AI assistant that lets users tune, control, and optimize their GeForce RTX PC, implying natural-language command operation, but it's labeled a 'Project' (experimental) with no detail on command scope or independent hands-on verification. Missing for 10: concrete examples of natural-language commands in action, confirmation G-Assist is generally available (not just a preview), and independent/community corroboration of its usability.", "evidenceIds": [ "nvidia-rtx-5080-docs-12" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "NVIDIA documents CUDA/cuTile as a public programming API for the GPU (docs-17–20), giving developers a way to programmatically drive the hardware's compute capabilities, and there's a developer portal (llms.txt) hinting at machine-readable docs. However, there's no evidence of an agent-friendly, structured API (no OpenAPI/swagger spec found, docs.md 404) tailored for AI-native/agentic control of the card's features like DLSS or Reflex. Missing for 10: a structured/machine-readable API spec (OpenAPI/swagger), evidence of agent-callable endpoints for GPU features, and independent confirmation of AI-native API usage.", "evidenceIds": [ "nvidia-rtx-5080-docs-17", "nvidia-rtx-5080-docs-18", "nvidia-rtx-5080-docs-19", "nvidia-rtx-5080-docs-20", "nvidia-rtx-5080-probe-1", "nvidia-rtx-5080-probe-2", "nvidia-rtx-5080-probe-3" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A consumer GPU is hardware; issuing scoped API credentials for agents is a cloud/software identity-management axis that does not apply to this product category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-sdks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "NVIDIA provides official developer SDKs for RTX GPUs including the CUDA Toolkit, cuTile Python/C++ tile programming APIs, and Nsight profiling tools, plus curated GPU-optimized SDKs spanning Windows ML, Ollama, and PyTorch backends — a clear AI-native build surface. Missing for 10: independent developer corroboration of SDK usability, working docs-as-markdown/OpenAPI endpoints (probes returned 404s), and concrete quickstart/tutorial evidence.", "evidenceIds": [ "nvidia-rtx-5080-docs-17", "nvidia-rtx-5080-docs-18", "nvidia-rtx-5080-docs-19", "nvidia-rtx-5080-docs-20", "nvidia-rtx-5080-docs-21", "nvidia-rtx-5080-probe-1", "nvidia-rtx-5080-probe-2", "nvidia-rtx-5080-probe-3" ] }, { "productId": "nvidia-rtx-5080", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "GeForce RTX 5080 is a consumer GPU hardware product, not a service or platform with an event/webhook subscription model; webhook subscriptions are a category error for this type of product.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "While the RTX 5080 ecosystem includes CUDA toolkit references, there is no evidence of an interactive, runnable API reference; probes explicitly show 404s for docs-md and OpenAPI/swagger specs, and no mention of interactive runnable examples anywhere in the docs.", "evidenceIds": [ "nvidia-rtx-5080-probe-2", "nvidia-rtx-5080-probe-3", "nvidia-rtx-5080-docs-19" ] }, { "productId": "nvidia-rtx-5080", "storyId": "api-machine-spec", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5080 is a consumer GPU hardware product, not an API/service platform; a machine-readable API spec axis does not apply to this kind of product. Probe evidence confirms no OpenAPI endpoint exists, but this is a category mismatch rather than a missing feature of an applicable axis.", "evidenceIds": [ "nvidia-rtx-5080-probe-3" ] }, { "productId": "nvidia-rtx-5080", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A sandbox environment for testing without touching production data is a software/platform concept irrelevant to a consumer GPU product like the RTX 5080; this is a category mismatch, not a missing feature.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5080 is a consumer GPU hardware product, not an API/service platform; versioned APIs with deprecation policies is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU is hardware; automation/event-trigger rule engines are an application-layer concern, not something a graphics card provides itself. G-Assist is an AI assistant for tuning but no evidence of rule-based event triggers.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "A GPU hardware product has no automation/workflow versioning, review, or rollback capability by design — this axis applies to software automation platforms, not a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "compute-toolchain-support", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "NVIDIA's developer portal documents the CUDA toolkit (compiler, libraries, Nsight profiling tools, new CUDA Tile programming model) as the compute toolchain for GeForce RTX GPUs, and the RTX 5080 is marketed as using Tensor Cores for AI/compute workloads, implying CUDA support. However, the evidence never explicitly names the RTX 5080 SKU on a supported-GPU compatibility list, and there's no independent hands-on confirmation of CUDA workloads running on this specific card. Missing for 10: an explicit CUDA supported-GPU list naming RTX 5080/Blackwell consumer parts, and independent developer reports of compute workloads (e.g., PyTorch/cuDNN) running on this card.", "evidenceIds": [ "nvidia-rtx-5080-docs-17", "nvidia-rtx-5080-docs-18", "nvidia-rtx-5080-docs-19", "nvidia-rtx-5080-docs-20", "nvidia-rtx-5080-docs-9" ] }, { "productId": "nvidia-rtx-5080", "storyId": "creator-media-acceleration", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs claim broad creator-app acceleration (video editing, 3D rendering, ComfyUI/AI workflows) via docs-11/docs-13, but there is no documentation of the specific hardware media engine specs (AV1/HEVC encode/decode capabilities) or any professional/ISV-certified driver program (e.g., Studio Driver certification, ISV app certifications) that the story asks about. Missing for 10: explicit AV1/HEVC NVENC/NVDEC engine specs, ISV certification list or Studio Driver certification documentation, independent corroboration of creator-app performance claims.", "evidenceIds": [ "nvidia-rtx-5080-docs-13", "nvidia-rtx-5080-docs-11" ] }, { "productId": "nvidia-rtx-5080", "storyId": "datacenter-scale-out", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack for the RTX 5080 covers gaming features (DLSS, Reflex, ray tracing) and general CUDA/developer tooling, but contains no mention of NVLink, Infinity Fabric, multi-GPU scaling, or rack-scale deployment — capabilities associated with datacenter-class parts, not this consumer GPU. Since the axis is a fair question for a GPU aimed at ML workloads but no supporting evidence exists, this is a 'none' verdict.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "efficient-sustained-workloads", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No documented TDP/power envelope specs or independent performance-per-watt benchmarks are present in the evidence pack; the only related community data point (nvidia-rtx-5080-comm-3) states the prior-gen 4080 Super actually has better performance-per-watt than the 5080, undermining rather than supporting an efficiency claim.", "evidenceIds": [ "nvidia-rtx-5080-comm-3" ] }, { "productId": "nvidia-rtx-5080", "storyId": "high-refresh-4k-gaming", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Vendor docs claim strong 4K performance via DLSS4/Multi Frame Generation, RT/Tensor cores, and Reflex responsiveness, and an independent review (TechPowerUp) corroborates 'good gaming performance' with all new RTX 50 features, plus an HN discussion noting substantially higher geomean benchmark scores vs prior gens. However, the independent evidence also flags gen-over-gen gains being smaller than expected and doesn't cite specific 4K high-refresh frame-rate benchmarks or resolution/refresh-specific numbers. Missing for 10: independent 4K high-refresh-rate benchmark data (e.g., specific FPS at 4K/144Hz across titles), and reviews directly validating DLSS4 frame-gen multiplier claims in real games.", "evidenceIds": [ "nvidia-rtx-5080-docs-1", "nvidia-rtx-5080-docs-8", "nvidia-rtx-5080-comm-1", "nvidia-rtx-5080-comm-2" ] }, { "productId": "nvidia-rtx-5080", "storyId": "linux-first-class", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains only Windows-oriented marketing pages, CUDA toolkit docs, and general community reviews/pricing discussion; none mention Linux driver releases, Linux driver documentation, or independent Linux benchmarking of the RTX 5080. Missing for 10: documented Linux driver release notes, Linux-specific support pages, and independent Linux hands-on/benchmark coverage.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "NVIDIA's own pages mention Tensor Cores and reference 'Windows ML, Ollama, PyTorch, and other inference backends' plus general AI-model support and CUDA toolkit, but there is no explicit documentation of FP8/FP4 precision support or named serving stacks like TensorRT-LLM, vLLM, or llama.cpp for the RTX 5080 specifically. missing for 10: explicit FP8/FP4 precision documentation, TensorRT-LLM/vLLM/llama.cpp integration details, independent benchmark corroboration of low-precision inference on this part.", "evidenceIds": [ "nvidia-rtx-5080-docs-21", "nvidia-rtx-5080-docs-9", "nvidia-rtx-5080-docs-19" ] }, { "productId": "nvidia-rtx-5080", "storyId": "memory-spec-bandwidth", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no memory specification details (VRAM capacity, memory type, bus width, or bandwidth) for the RTX 5080; all docs focus on DLSS, RT/Tensor cores, CUDA toolkit, and software features. Missing for 10: VRAM capacity, memory type (e.g. GDDR7), bus width, and bandwidth figures for this specific part.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "ml-framework-support", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "The product page explicitly claims RTX 50-series GPUs work with PyTorch and other inference backends, and NVIDIA's developer docs describe the CUDA toolkit (compiler, libraries, profiling tools) that underlies ML framework support, but no explicit PyTorch version/support matrix, CUDA compute-capability listing, or install instructions specific to RTX 5080 are provided. missing for 10: an official PyTorch/CUDA compatibility matrix for RTX 5080 (e.g., supported CUDA/cuDNN versions), first-party install docs, and independent hands-on confirmation that mainstream frameworks run correctly on this card.", "evidenceIds": [ "nvidia-rtx-5080-docs-21", "nvidia-rtx-5080-docs-19", "nvidia-rtx-5080-docs-17", "nvidia-rtx-5080-docs-18" ] }, { "productId": "nvidia-rtx-5080", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of open-source kernel modules or vendor-documented upstream Linux driver support for RTX 5080; all evidence pertains to proprietary DLSS, CUDA toolkit, and marketing features, not driver openness.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5080 is a consumer GPU hardware product with a UI (NVIDIA app, drivers) but no API/UI parity concept applies — it's not a software service with dual API/UI interfaces to compare. This axis is a category error for a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5080 is a hardware GPU product, not a data-storing SaaS/platform with user account data to export; data export/portability is not a relevant axis for a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains only DLSS/AI feature marketing, CUDA/dev tools, and general pricing/performance commentary — no published TDP/TGP figures, power connector (12VHPWR) specs, PSU wattage recommendations, or cooling/thermal guidance for the RTX 5080 appear anywhere. missing for 10: TDP/TGP spec, connector/PSU requirements, case/cooling guidance, thermal design docs.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "GeForce RTX 5080 is a consumer GPU hardware product; data residency/region storage is a cloud-service/SaaS concern, not an axis applicable to a local graphics card. Local AI processing is mentioned (docs-10, docs-11) but this pertains to local vs cloud processing, not regional data storage choice.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX 5080 is a consumer GPU hardware product, not a data-processing service or platform that retains user data; data retention/deletion controls are a SaaS/cloud-service concern, not applicable to a graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX 5080 is a consumer GPU hardware product, not a software service with account/telemetry settings of the kind this story addresses; opting out of telemetry/usage tracking is not a fair axis for a graphics card itself.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "run-70b-local-llm", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains no published VRAM capacity or memory-bandwidth figures for the RTX 5080, nor any specific claim about running 70B-class quantized LLMs; it only lists generic AI/gaming feature marketing (DLSS, Ollama/PyTorch support mentions) without capacity numbers. Missing for 10: VRAM size specification, memory bandwidth specification, any benchmark or claim about large-model (70B) local inference feasibility.", "evidenceIds": [ "nvidia-rtx-5080-docs-21", "nvidia-rtx-5080-docs-10" ] }, { "productId": "nvidia-rtx-5080", "storyId": "tensor-throughput-disclosed", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence pack contains only marketing/DLSS/CUDA toolkit descriptions and community pricing/performance commentary; no published FP16/FP32/INT8/sparsity TFLOPS or TOPS figures for the RTX 5080 are cited anywhere, so an ML engineer cannot size training/inference from stated precision-tagged throughput numbers.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5080", "storyId": "upscaling-frame-generation", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "NVIDIA docs confirm DLSS 4 with Multi Frame Generation, Super Resolution, Ray Reconstruction, and DLAA are supported on RTX 50 Series cards including the 5080, with NVIDIA app support to update hundreds of games to the latest DLSS features. Community/independent review corroborates real-world gaming performance gains. Missing for 10: independent per-game compatibility list or third-party benchmark specifically isolating frame-gen quality/artifacts across many titles.", "evidenceIds": [ "nvidia-rtx-5080-docs-1", "nvidia-rtx-5080-docs-2", "nvidia-rtx-5080-docs-4", "nvidia-rtx-5080-docs-6", "nvidia-rtx-5080-comm-1" ] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-agent-docs", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "NVIDIA's developer portal serves a valid llms.txt (HTTP 200, descriptive content) that an agent could use to discover CUDA/GPU-related docs, but this is at the general developer.nvidia.com domain rather than an RTX-5090-specific surface, and adjacent agent-friendly affordances (markdown doc exports, OpenAPI spec) are absent (404s). missing for 10: RTX-5090-specific llms.txt or agent doc entry point, working markdown/API exports, independent confirmation agents actually use this pathway successfully.", "evidenceIds": [ "nvidia-rtx-5090-probe-1", "nvidia-rtx-5090-probe-2", "nvidia-rtx-5090-probe-3" ] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not an automation/agent platform; setting up background-running automations is an application/software-layer capability entirely outside the scope of a graphics card, making this a category error rather than a missing feature.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-headless", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a GPU hardware product, not an agent or platform that consumes MCP servers; plugging in MCP tool servers is a software/agent-layer capability entirely outside this product's category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not an agentic software platform or service; connecting agents via an official MCP server is outside its product category — a category error, not a missing feature.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-public-api", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "CUDA Toolkit documentation describes a programmatic API (CUDA, cuTile Python/C++) for building GPU-accelerated applications, which is the closest analog to a 'documented public API' for this hardware product, but this is a low-level compute-kernel API, not an agent-drivable control interface, and direct probes found no OpenAPI/Swagger spec or machine-readable docs (404s). Missing for 10: a structured/machine-readable API spec (OpenAPI/REST), any agentic control surface, and independent corroboration of AI-native programmatic access beyond raw CUDA kernel programming.", "evidenceIds": [ "nvidia-rtx-5090-docs-1", "nvidia-rtx-5090-docs-3", "nvidia-rtx-5090-probe-2", "nvidia-rtx-5090-probe-3" ] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not an API/service platform; scoped API credential issuance is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-sdks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "NVIDIA provides official SDKs (CUDA Toolkit, CUDA Tile C++/cuTile Python, RTX Neural Shaders SDK, RTX Mega Geometry, Nsight tools) that developers can build AI/graphics applications against, all documented on NVIDIA's developer portal. Missing for 10: independent/hands-on developer corroboration of building against these SDKs, and deeper API reference/sample documentation beyond marketing-style descriptions.", "evidenceIds": [ "nvidia-rtx-5090-docs-1", "nvidia-rtx-5090-docs-2", "nvidia-rtx-5090-docs-3", "nvidia-rtx-5090-docs-14", "nvidia-rtx-5090-docs-15", "nvidia-rtx-5090-docs-4" ] }, { "productId": "nvidia-rtx-5090", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not a service or platform with event-driven subscription APIs; webhooks are a category error for this kind of product.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "api-interactive-docs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not an API/SDK service; an interactive API reference with runnable examples is a category error for this axis. Evidence shows no such interactive reference exists (openapi/docs probes return 404), reinforcing this is not applicable to the hardware product itself.", "evidenceIds": [ "nvidia-rtx-5090-probe-2", "nvidia-rtx-5090-probe-3" ] }, { "productId": "nvidia-rtx-5090", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "This is a consumer GPU product; while NVIDIA's developer portal exists, an explicit probe found no OpenAPI/machine-readable API spec (all candidate paths 404), so there's no evidence of a downloadable machine-readable API spec.", "evidenceIds": [ "nvidia-rtx-5090-probe-3" ] }, { "productId": "nvidia-rtx-5090", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not a software/service platform with sandbox vs production environments; testing against sandbox data is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not an API/service platform; versioned APIs and deprecation policies are not applicable to this product category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The GeForce RTX 5090 is a consumer GPU hardware product, not an automation/workflow platform; defining event-triggered rules is a category error for this axis - no evidence pack material addresses rule-based automation.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not an automation/workflow platform; versioning, reviewing, and rolling back automations is a software/orchestration concept entirely outside a GPU's product category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "compute-toolchain-support", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "NVIDIA's CUDA Toolkit docs explicitly target GeForce RTX GPUs including Blackwell-architecture cards like the 5090, and independent Phoronix benchmarks (cited via comm-8) confirm real compute workloads running on the 5090 with 1.42x uplift over the 4090 across 60+ compute benchmarks, corroborating that it functions as a genuine CUDA compute target. Missing for 10: no explicit CUDA compute-capability/SM version listing naming '5090' directly in vendor docs, and no independent report specifically validating professional GPU-compute (non-gaming) toolchains beyond Phoronix's Linux compute suite.", "evidenceIds": [ "nvidia-rtx-5090-docs-1", "nvidia-rtx-5090-docs-2", "nvidia-rtx-5090-docs-3", "nvidia-rtx-5090-docs-22", "nvidia-rtx-5090-comm-8" ] }, { "productId": "nvidia-rtx-5090", "storyId": "creator-media-acceleration", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers CUDA, DLSS, RT Cores, and gaming features but contains no documentation of NVENC/AV1/HEVC hardware encoders, Studio Driver certification, or ISV-certified professional app support for the RTX 5090. Missing for 10: any mention of hardware encoder specs, Studio Driver program, or ISV certification for creator applications.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "datacenter-scale-out", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no mention of NVLink, Infinity Fabric, multi-GPU interconnect, or rack-scale deployment for the RTX 5090; all specs describe a single consumer GPU (GDDR7, PCIe 5.0) and community commentary discusses it only as a DIY/local-LLM card, not datacenter-scale training/serving infrastructure.", "evidenceIds": [ "nvidia-rtx-5090-docs-18", "nvidia-rtx-5090-comm-1", "nvidia-rtx-5090-comm-5" ] }, { "productId": "nvidia-rtx-5090", "storyId": "efficient-sustained-workloads", "verdict": "disputed", "quality": 5, "confidence": "medium", "rationale": "NVIDIA's spec page exposes board power/TOPS figures (probe-rt-1) but no explicit efficiency/perf-per-watt marketing claim is cited; independent testing (Phoronix via HN, comm-6/7/8) directly contradicts any efficiency narrative, finding RTX 5090 perf-per-watt is similar to or worse than the 4080/4090 despite higher absolute performance, and Tom's Hardware also flags thermal/power concerns (comm-2). Missing for 10: a documented power-envelope/TDP spec sheet from NVIDIA explicitly framed for ML workloads, and independent perf-per-watt benchmarks that confirm rather than undercut efficiency gains.", "evidenceIds": [ "nvidia-rtx-5090-probe-rt-1", "nvidia-rtx-5090-comm-6", "nvidia-rtx-5090-comm-7", "nvidia-rtx-5090-comm-8", "nvidia-rtx-5090-comm-2" ] }, { "productId": "nvidia-rtx-5090", "storyId": "high-refresh-4k-gaming", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Independent Tom's Hardware and Phoronix benchmarks corroborate the RTX 5090 as the fastest consumer GPU (1.42x over 4090), supporting vendor claims of top-tier gaming performance, and NVIDIA's DLSS/Reflex/path-tracing feature docs target high-refresh 4K use cases. However, community evidence raises real caveats: reviewers flag Founders Edition thermal issues, similar-or-worse perf-per-watt vs 4080/4090, and specifically critique Multi Frame Generation as 'marketing' since input sampling doesn't scale with the reported FPS boost, undercutting the headline frame-rate claims. missing for 10: dedicated 4K high-refresh benchmark suites (e.g. specific FPS-at-4K numbers across many AAA titles), resolution of the MFG input-latency criticism, and confirmation thermal/efficiency issues don't limit sustained high-refresh performance.", "evidenceIds": [ "nvidia-rtx-5090-docs-5", "nvidia-rtx-5090-docs-12", "nvidia-rtx-5090-docs-13", "nvidia-rtx-5090-comm-1", "nvidia-rtx-5090-comm-2", "nvidia-rtx-5090-comm-3", "nvidia-rtx-5090-comm-8" ] }, { "productId": "nvidia-rtx-5090", "storyId": "linux-first-class", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Independent Phoronix benchmarking confirms the RTX 5090 is tested and functional on Linux across 60+ compute workloads, showing real-world Linux usability, but the evidence pack contains no first-party documentation of dedicated Linux driver releases, changelogs, or Linux-specific support pages from NVIDIA. Missing for 10: documented NVIDIA Linux driver release notes/changelog, official Linux support/compatibility docs, broader independent Linux gaming/compute test corroboration beyond one Phoronix reference.", "evidenceIds": [ "nvidia-rtx-5090-comm-8" ] }, { "productId": "nvidia-rtx-5090", "storyId": "llm-inference-stack", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "NVIDIA's RTX documentation confirms fifth-gen Tensor Cores with FP4/FP8/FP6 low-precision support for deep learning workloads, and community discussion references RTX 5090 use for local LLM inference (though cost concerns are raised). However, there is no evidence of explicit support/documentation for named serving stacks like TensorRT-LLM, vLLM, or llama.cpp on this specific part. Missing for 10: documented compatibility with TensorRT-LLM, vLLM, or llama.cpp serving frameworks, benchmark data showing actual inference throughput on this GPU.", "evidenceIds": [ "nvidia-rtx-5090-docs-22", "nvidia-rtx-5090-comm-5", "nvidia-rtx-5090-docs-18" ] }, { "productId": "nvidia-rtx-5090", "storyId": "memory-spec-bandwidth", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "NVIDIA's own product page confirms capacity (32GB) and memory type (GDDR7), and community review corroborates a 512-bit bus, but no evidence pack item states the exact memory bandwidth figure (GB/s) for this part. Missing for 10: an explicit published bandwidth number (GB/s) from a first-party spec sheet.", "evidenceIds": [ "nvidia-rtx-5090-docs-18", "nvidia-rtx-5090-comm-1", "nvidia-rtx-5090-probe-rt-1" ] }, { "productId": "nvidia-rtx-5090", "storyId": "ml-framework-support", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Evidence confirms NVIDIA ships a general CUDA Toolkit and Tensor Core architecture details relevant to deep learning, but there is no explicit official PyTorch/TensorFlow support matrix, compute-capability listing, or documented install path referencing RTX 5090/Blackwell specifically; a community post even flags 5090 as impractical for local LLM work due to price, not toolchain issues. missing for 10: explicit PyTorch build/support-matrix docs naming RTX 5090 or Blackwell (sm_120) compute capability, cuDNN/cuBLAS version compatibility notes, and independent confirmation of PyTorch working out-of-the-box on the card.", "evidenceIds": [ "nvidia-rtx-5090-docs-1", "nvidia-rtx-5090-docs-22", "nvidia-rtx-5090-comm-5" ] }, { "productId": "nvidia-rtx-5090", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no mention of open-source kernel modules (e.g., NVIDIA's open GPU kernel module project) or upstream Linux driver support for the RTX 5090; all driver-related community mentions are about closed-driver bugs/issues, not open-source availability. This is a fair axis for a GPU (vendors like NVIDIA do ship open kernel modules), but nothing in the evidence documents it for this card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer graphics hardware product, not a UI/API software product; there is no 'UI' vs 'API' parity concept applicable to a physical GPU—this axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a GPU hardware product with no user account, data storage, or data-export concept; 'exporting data in open formats and leaving' is a SaaS/platform lock-in axis that doesn't apply to a physical graphics card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "power-spec-planning", "verdict": "disputed", "quality": 4, "confidence": "medium", "rationale": "Evidence confirms NVIDIA's official 5090 page exposes board-power and spec data (probe-rt-1) and the community references the 12VHPWR/12V-2x6 connector spec, but no evidence pack item actually cites the published TDP/TGP wattage, PSU wattage recommendation, or explicit cooling/case guidance for this exact card. More importantly, community evidence (comm-4) documents real-world 12VHPWR connector melting incidents tied to the card's power-connector design, and comm-2 reports the Founders Edition running hot — concretely undermining confidence that a build spec'd purely around the vendor's connector/cooling guidance will be reliable. missing for 10: explicit first-party TDP/TGP wattage figure, official PSU/connector spec sheet, first-party cooling/case airflow guidance, resolution of the connector-melting safety concern.", "evidenceIds": [ "nvidia-rtx-5090-probe-rt-1", "nvidia-rtx-5090-comm-4", "nvidia-rtx-5090-comm-2", "nvidia-rtx-5090-docs-18" ] }, { "productId": "nvidia-rtx-5090", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product with no data-hosting or cloud service component; data residency/region storage choice is not an applicable axis for local hardware.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer GPU hardware product, not a data-processing service or platform that collects/retains user data; data retention/deletion controls are not a meaningful axis for a piece of silicon.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX 5090 is a consumer graphics card, not a service or software platform that collects user telemetry to opt out of; this privacy-posture axis about opting out of usage tracking applies to software/SaaS products, not GPU hardware.", "evidenceIds": [] }, { "productId": "nvidia-rtx-5090", "storyId": "run-70b-local-llm", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "NVIDIA publishes the RTX 5090's 32GB GDDR7 VRAM (docs-18) and strong Tensor Core throughput (docs-22), which is enough for some 70B-class models at aggressive quantization, but the evidence never states a specific memory-bandwidth figure and community discussion (comm-5) notes the GPU's price makes it 'insignificant for the DIY crowd' compared to used 3090s or Mac unified memory for local LLM work, undercutting the 'practical' framing. Missing for 10: published memory-bandwidth spec (GB/s), explicit vendor guidance on running 70B-class quantized models, and independent benchmarks confirming inference throughput at that scale.", "evidenceIds": [ "nvidia-rtx-5090-docs-18", "nvidia-rtx-5090-docs-22", "nvidia-rtx-5090-comm-5" ] }, { "productId": "nvidia-rtx-5090", "storyId": "tensor-throughput-disclosed", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs mention fifth-gen Tensor Cores with specific precision support (FP4, TF32, BF16, FP16, FP8, FP6) and a relative claim of 'up to 3x higher throughput,' but no evidence gives an absolute TFLOPS/TOPS figure paired with sparsity state — the story explicitly wants a non-bare number with precision AND sparsity documented. Missing for 10: dense vs sparse TOPS/TFLOPS tables per precision, official spec-sheet numeric throughput figures, and independent benchmark corroboration of those numbers for ML workload sizing.", "evidenceIds": [ "nvidia-rtx-5090-docs-22", "nvidia-rtx-5090-probe-rt-1" ] }, { "productId": "nvidia-rtx-5090", "storyId": "upscaling-frame-generation", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "NVIDIA's official docs detail DLSS 4 with Multi Frame Generation, Super Resolution, Ray Reconstruction, and DLAA on RTX 5090/50-series, plus broad game rollout via the NVIDIA app supporting hundreds of titles. Independent review (Tom's Hardware) confirms these features work in practice, though it flags marketing caveats around Multi Frame Generation's input latency implications. Missing for 10: independent per-game compatibility list/count and more third-party benchmarking corroboration beyond one review.", "evidenceIds": [ "nvidia-rtx-5090-docs-5", "nvidia-rtx-5090-docs-6", "nvidia-rtx-5090-docs-7", "nvidia-rtx-5090-docs-8", "nvidia-rtx-5090-docs-9", "nvidia-rtx-5090-docs-10", "nvidia-rtx-5090-comm-1", "nvidia-rtx-5090-comm-3" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-agent-docs", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "A probe confirms developer.nvidia.com/llms.txt returns HTTP 200 with a description of NVIDIA's developer portal, showing an agent could be pointed at an llms.txt-style resource covering NVIDIA's AI/dev ecosystem. However this is a generic NVIDIA-wide file, not RTX PRO 6000-specific, and companion probes (docs-md, openapi) 404, indicating no broader agent-oriented documentation surface. Missing for 10: product-specific agent-readable docs, markdown/API doc mirrors, and any first-party mention of llms.txt support.", "evidenceIds": [ "nvidia-rtx-pro-6000-probe-1", "nvidia-rtx-pro-6000-probe-2", "nvidia-rtx-pro-6000-probe-3" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-autonomous-automation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This is a GPU hardware product; setting up autonomous background automations is a software/orchestration-layer capability, not something a GPU itself provides. This axis is a category error for a hardware accelerator.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-headless", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence describes local desktop AI workflows, CUDA toolkit, and MIG partitioning, but nothing addresses running the GPU headlessly (no display) in a CI/automation pipeline. Since GPUs are commonly deployed headlessly in server/CI environments, the axis applies, but no evidence of headless operation, driver support without display, or CI integration is provided.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a GPU hardware product, not an application or agent platform; plugging in MCP servers is a software/agent-integration axis that doesn't apply to a workstation GPU itself.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a hardware GPU product, not an agent or software service; MCP server connectivity is a software integration axis that doesn't apply to a physical GPU workstation card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence describes CUDA toolkit components (compiler, libraries, debugging tools) but never explicitly documents an official CLI tool (e.g., nvidia-smi or similar) for AI-native/agentic workflows tied to this GPU.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-public-api", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence documents CUDA as a programming toolkit for writing GPU-accelerated software, but there is no documented public API for programmatically 'driving' the RTX PRO 6000 itself (e.g., management/control API for agentic automation), and probes explicitly found no OpenAPI/swagger spec (404s) for the developer portal.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-3", "nvidia-rtx-pro-6000-probe-3", "nvidia-rtx-pro-6000-probe-2" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a hardware GPU product; issuing scoped API credentials for agents is a software/IAM capability entirely outside the scope of a physical GPU card's product category.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-sdks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "NVIDIA provides well-documented official SDKs to build against (CUDA Toolkit with compiler/runtime/libraries, cuTile Python, RTX Neural Shaders SDK) that target this GPU's architecture, giving AI-native developers a real path to build agentic/AI workloads. However, community hands-on discussion notes the card's SM120 architecture lacks support for key CUDA primitives (tmem/tcgen05) in main libraries, indicating real gaps in SDK/library readiness beyond the marketing claims. Missing for 10: independent developer corroboration of successful SDK integration, resolution of the SM120 library-support gap, and clearer documentation of which SDK features are actually usable on this specific card.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-3", "nvidia-rtx-pro-6000-docs-4", "nvidia-rtx-pro-6000-docs-5", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "agentic-webhooks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a physical GPU/hardware product, not a service or platform with an event-driven API; webhooks are a category error for this axis.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "api-interactive-docs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a physical GPU hardware product, not an API/SaaS platform; there is no product-specific API for it to document. Evidence pack shows no API reference at all, and probes confirm no OpenAPI spec exists — this axis is a category error for a GPU hardware SKU.", "evidenceIds": [ "nvidia-rtx-pro-6000-probe-3" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "api-machine-spec", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a physical GPU/workstation hardware product, not a web service or API platform; the notion of a downloadable OpenAPI/machine-readable API spec is a category mismatch for a hardware SKU, even though NVIDIA's broader developer ecosystem includes SDKs like CUDA. Probe evidence confirms no OpenAPI/swagger endpoints exist for this product page, reinforcing that this axis doesn't fit a hardware product line.", "evidenceIds": [ "nvidia-rtx-pro-6000-probe-3", "nvidia-rtx-pro-6000-probe-2" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a physical workstation GPU; sandbox-vs-production data isolation for testing AI agents is a software/platform concept not applicable to hardware silicon, which only provides compute (and optionally MIG partitioning) rather than data environment separation.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "api-versioning-policy", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "The RTX PRO 6000 is a physical GPU/hardware product, not an API or SaaS service; versioned APIs with deprecation policies is a category mismatch for a hardware product's own axis (CUDA toolkit versioning belongs to NVIDIA's software platform, not the GPU product itself).", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "automation-rules-engine", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a GPU hardware product; defining event-triggered automation rules is an application/software-layer capability, not something a GPU itself provides. This axis is a category error for a hardware product.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a GPU hardware product; versioning/reviewing/rolling back automations is a software workflow-management concern entirely outside the scope of a GPU's capabilities.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "compute-toolchain-support", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "NVIDIA's official CUDA Toolkit and CUDA-X docs explicitly list this GPU class as a supported target, and community usage (41k tok/s LLM inference) confirms real-world CUDA workload deployment. A minor caveat exists: one hands-on report notes SM120 lacks tmem/tcgen05 support in some main libraries, indicating partial feature-level gaps rather than a full contradiction of the toolchain-support claim. missing for 10: independent benchmark/library compatibility matrix confirming full CUDA feature parity across major frameworks.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-2", "nvidia-rtx-pro-6000-docs-3", "nvidia-rtx-pro-6000-docs-4", "nvidia-rtx-pro-6000-comm-1", "nvidia-rtx-pro-6000-comm-4", "nvidia-rtx-pro-6000-probe-rt-1" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "creator-media-acceleration", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Vendor docs explicitly claim enhanced AV1/H.265 (HEVC) encode/decode support aimed at livestreaming and real-time editing, plus general creator-app acceleration (3D modeling, animation, virtual production). However, there is no ISV-certification detail (no Studio driver or specific creative-app certification list) and no independent/hands-on verification of media-engine performance for creators. Missing for 10: ISV-certified driver documentation (e.g., Studio Driver certifications for specific creative apps), independent benchmarks of AV1/HEVC encode quality, and confirmation of number/type of NVENC/NVDEC engines.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-9", "nvidia-rtx-pro-6000-docs-8" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "datacenter-scale-out", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains no documentation of NVLink, Infinity Fabric, or any high-bandwidth multi-GPU interconnect for the RTX PRO 6000; it is positioned as a single-card workstation GPU (PCIe Gen5, desktop form factor) rather than a rack-scale datacenter part. Community threads even contrast it unfavorably with true datacenter GPUs (e.g., B300) citing lack of tensor-memory/library support and treat multi-card setups as just several discrete cards drawing 2.4kW, not a unified interconnect fabric.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-13", "nvidia-rtx-pro-6000-comm-2", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "efficient-sustained-workloads", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no documented power envelope specs (TDP) from NVIDIA nor any independent performance-per-watt benchmarking; community discussion only raises concerns about high power draw (~600W/card implied from a 2.4kW quad-card setup) without any efficiency testing. Axis applies to a workstation GPU aimed at ML engineers, but no supporting evidence exists.", "evidenceIds": [ "nvidia-rtx-pro-6000-comm-2", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "high-refresh-4k-gaming", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "linux-first-class", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack specifically documents Linux driver releases or independent Linux benchmarking/testing of the RTX PRO 6000; the docs cite CUDA toolkit generically (cross-platform) and community threads discuss pricing, power draw, and hardware defects rather than Linux driver support or Linux-specific testing.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "llm-inference-stack", "verdict": "disputed", "quality": 4, "confidence": "medium", "rationale": "NVIDIA's docs only vaguely reference AI/LLM use (memory capacity, CUDA-X libraries) without naming FP8/FP4 precision support or specific serving stacks like TensorRT-LLM, vLLM, ROCm, or llama.cpp for this part. Community evidence shows real inference throughput (41k tok/s) but also a concrete technical objection that the card's SM120 architecture lacks tmem/tcgen05 and has 'lack of support in main libraries', directly contradicting a clean 'supported serving stack' story. Missing for 10: explicit vendor documentation naming FP8/FP4 support and specific serving-stack compatibility (TensorRT-LLM, vLLM, llama.cpp), and resolution of the community-reported library support gaps.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-1", "nvidia-rtx-pro-6000-comm-1", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "memory-spec-bandwidth", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Official NVIDIA pages confirm 96GB GPU memory capacity clearly, but the evidence pack contains no first-party bus-width or bandwidth figures, and memory type (GDDR7) is only mentioned in a community comment, not vendor spec docs. missing for 10: bus width spec, bandwidth (GB/s) spec, vendor-confirmed memory type in official docs.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-1", "nvidia-rtx-pro-6000-docs-12", "nvidia-rtx-pro-6000-comm-7" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "ml-framework-support", "verdict": "disputed", "quality": 4, "confidence": "medium", "rationale": "NVIDIA's docs claim CUDA-X/CUDA toolkit compatibility and general AI/LLM workflows on the card, and community reports do show people running LLM inference workloads (41k tok/s) on RTX PRO 6000 Blackwell units, suggesting frameworks do run. However, a specific hands-on/community comment states these cards are SM120 architecture 'so no tmem/tcgen05 and lack of support in main libraries,' directly contradicting the notion of seamless official framework support via standard build/support matrices. Missing for 10: an explicit official PyTorch/framework support matrix or release notes confirming Blackwell SM120 compatibility, and resolution of the tcgen05/tmem library-support gap raised by users.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-2", "nvidia-rtx-pro-6000-docs-3", "nvidia-rtx-pro-6000-comm-1", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "open-source-driver", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions open-source kernel modules, nouveau, or upstream Linux driver support for the RTX PRO 6000; all docs cite proprietary CUDA/RTX toolkits and community discussion focuses on power, pricing, and hardware defects rather than driver openness. Missing for 10: any vendor documentation of open-source GPU kernel modules or upstream kernel support for this card.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a physical GPU/hardware product, not a SaaS or software platform with a distinct UI and API surface to compare for parity; this story's premise (UI vs API feature parity) is a category error for a hardware accelerator.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "openness-full-export", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a GPU hardware component, not a data-storage or SaaS platform that holds user data to export; the 'export data and leave' axis is a category error for a workstation GPU.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "openness-self-host", "verdict": "full", "quality": 7, "confidence": "high", "rationale": "The RTX PRO 6000 is a physical GPU designed explicitly for local, on-premises AI workloads—running LLMs, agents, and data science locally 'without relying on costly cloud or data center resources,' with community evidence confirming real users self-hosting multi-GPU inference rigs achieving high token throughput. This is inherently self-hostable since it's hardware you own and run yourself. missing for 10: no first-party self-hosting guide/reference architecture, no independent benchmark validating claimed ease of self-hosted deployment at scale, and community notes highlight real friction (power/cooling requirements, hardware defects) that complicate the self-hosting experience.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-1", "nvidia-rtx-pro-6000-docs-10", "nvidia-rtx-pro-6000-comm-1", "nvidia-rtx-pro-6000-comm-2" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "power-spec-planning", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains only marketing copy about memory, CUDA-X, ray tracing, and I/O, with no official TDP/TGP figures, power-connector specifications, or cooling guidance for the RTX PRO 6000. Community threads mention very high real-world power draw (2.4kW across four cards) and cooling/VRM issues, but these are anecdotal complaints, not published board-power specs a gamer could use to plan a PSU or case cooling.", "evidenceIds": [ "nvidia-rtx-pro-6000-comm-2", "nvidia-rtx-pro-6000-comm-1" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a local workstation GPU where data stays on the user's own machine; there is no cloud service or multi-region deployment concept, so 'choosing a data storage region' is not a meaningful axis for this hardware product.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "privacy-no-training", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "The product is a local workstation GPU, and NVIDIA markets it as enabling AI workloads to run 'locally and securely' without sending data to the cloud, which implicitly prevents data from being sent to a third party for training. However, there is no explicit privacy-control feature, data-usage policy, or opt-out mechanism documented — the claim is only an indirect byproduct of local compute. Missing for 10: an explicit data-training opt-out or privacy policy statement, independent confirmation that no telemetry/data leaves the device, and any documentation addressing data governance for AI workloads.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-1" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "privacy-retention-controls", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a local workstation GPU; it does not act as a service that stores or processes user data on the vendor's behalf, so 'data retention and deletion' controls (a SaaS/cloud privacy concept) is a category mismatch for a hardware product used locally.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "privacy-telemetry-optout", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "RTX PRO 6000 is a hardware GPU product; telemetry opt-out/usage tracking settings are a software/SaaS privacy concern that doesn't apply to a physical GPU component itself.", "evidenceIds": [] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "run-70b-local-llm", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "NVIDIA docs explicitly market the 96 GB GDDR7 memory as enabling local LLM fine-tuning and agent workloads, and community evidence (HN thread) shows real users running multi-GPU RTX PRO 6000 setups for high-throughput LLM inference (tens of thousands of tok/s), confirming practical single-node large-model inference. Missing for 10: an explicit published memory-bandwidth (GB/s) figure and a documented single-card 70B-quantized benchmark rather than a 4-GPU aggregate.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-1", "nvidia-rtx-pro-6000-docs-12", "nvidia-rtx-pro-6000-comm-1", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "tensor-throughput-disclosed", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains only generic marketing claims (96GB memory, CUDA-X libraries, PCIe Gen5, display specs) and community pricing/power discussions, but no published TFLOPS/TOPS figures broken out by precision (FP16/FP8/INT8) or sparsity state anywhere in the docs or community threads. A GPU spec sheet is exactly the kind of product where such throughput tables are expected, so the axis applies but is unmet.", "evidenceIds": [ "nvidia-rtx-pro-6000-docs-1", "nvidia-rtx-pro-6000-docs-12", "nvidia-rtx-pro-6000-comm-4" ] }, { "productId": "nvidia-rtx-pro-6000", "storyId": "upscaling-frame-generation", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers workstation/AI/data-science features, memory, connectivity, and CUDA tooling, but never mentions DLSS, FSR, frame generation, or game-specific driver/game support for the RTX PRO 6000. This is a plausible axis for any RTX-branded GPU, so absence of documentation is 'none' rather than 'na'. missing for 10: DLSS/FSR version support documentation, frame generation capability, game compatibility list or driver notes.", "evidenceIds": [] } ]