GeForce RTX 5070 Ti vs Radeon RX 9070 XT
GeForce RTX 5070 Ti
NVIDIA Corporation
Radeon RX 9070 XT wins · 6–10 (11 drawn)
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
Agent access
ai-native userPoint an agent at llms.txt or agent-oriented docs
weight 2 · round to Radeon RX 9070 XTA 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).
- [probe] “PROBE llms.txt: HTTP 200 at https://developer.nvidia.com/llms.txt # NVIDIA Developer > Comprehensive developer portal for NVIDIA accelerate…”
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.
- [probe] “PROBE llms.txt: HTTP 200 at https://rocm.docs.amd.com/llms.txt # ROCm documentation > Note: ROCm documentation is split across multiple pro…”
- [probe] “PROBE openapi: all candidate paths 404 (https://rocm.docs.amd.com/openapi.json, https://rocm.docs.amd.com/swagger.json, https://rocm.docs.am…”
- [claimed-docs] “PyTorch on Windows updated with ROCm 7.2.1 on AMD Radeon graphics products and AMD Ryzen AI processors.”
ai-native userRun the product headlessly / in CI for automation
weight 2 · round to Radeon RX 9070 XTGeForce RTX 5070 Tinone0/10The 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.)
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.
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “OS Support Windows 10 - 64-Bit Edition , Windows 11 - 64-Bit Edition , Linux x86 64-Bit”
ai-native userUse an official CLI
weight 2 · round drawnGeForce RTX 5070 Tinone0/10The 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.)
ai-native userDrive the product through a documented public API
weight 3 · round to Radeon RX 9070 XTGeForce RTX 5070 Tinone0/10The 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.)
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.
- [claimed-docs] “PyTorch on Windows updated with ROCm 7.2.1 on AMD Radeon graphics products and AMD Ryzen AI processors.”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [probe] “PROBE llms.txt: HTTP 200 at https://rocm.docs.amd.com/llms.txt # ROCm documentation > Note: ROCm documentation is split across multiple pro…”
- [probe] “PROBE openapi: all candidate paths 404 (https://rocm.docs.amd.com/openapi.json, https://rocm.docs.amd.com/swagger.json, https://rocm.docs.am…”
ai-native userBuild against official SDKs
weight 2 · round drawnNVIDIA 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.
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
- [claimed-docs] “CUDA Tile C++ is an expression of the CUDA Tile programming model in C++... allows you to write tile kernels in C++”
- [claimed-docs] “cuTile Python is an expression of the CUDA Tile programming model in Python... allows you to write tile kernels in Python”
- [claimed-docs] “NVIDIA Nsight Compute and Nsight System suite of tools designed to help developers optimize and increase performance of their applications”
- [probe] “PROBE llms.txt: HTTP 200 at https://developer.nvidia.com/llms.txt # NVIDIA Developer > Comprehensive developer portal for NVIDIA accelerate…”
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.
- [claimed-docs] “PyTorch on Windows updated with ROCm 7.2.1 on AMD Radeon graphics products and AMD Ryzen AI processors.”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Windows® PyTorch”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “The same ROCm™ stack that powers your desktop development on RDNA™ architecture GPUs also supports AMD Instinct™ accelerators on CDNA™ archi…”
- [probe] “PROBE llms.txt: HTTP 200 at https://rocm.docs.amd.com/llms.txt # ROCm documentation > Note: ROCm documentation is split across multiple pro…”
Agentic features
ai-native userGet AI-generated insights and suggestions from my data inside the product
weight 2 · round to GeForce RTX 5070 TiNVIDIA'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.
- [claimed-docs] “NVIDIA Project G-Assist is an AI assistant powered by your GeForce RTX PC that helps you tune, control, and optimize your system”
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
ai-native userDelegate tasks to a built-in AI assistant inside the product
weight 3 · round to GeForce RTX 5070 TiNVIDIA 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.
- [claimed-docs] “NVIDIA Project G-Assist is an AI assistant powered by your GeForce RTX PC that helps you tune, control, and optimize your system”
ai-native userOperate the product with natural-language commands
weight 2 · round drawnGeForce RTX 5070 Tinone0/10The 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.)
Ai compute — stories about ai compute in this arenaAi compute
Stories about ai compute in this arena
Inference stack
ai-native userThis GPU has a documented LLM inference story — low-precision formats (FP8/FP4) and supported serving stacks (TensorRT-LLM, vLLM, ROCm, llama.cpp) for this part
weight 3 · round to Radeon RX 9070 XTThe 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.
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
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.
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Windows® PyTorch”
Tensor specs
ml engineerSize training and inference from published tensor throughput — TFLOPS or TOPS with precision and sparsity stated, not a bare marketing number
weight 3 · round drawnGeForce RTX 5070 Tinone0/10The 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.
- [claimed-docs] “Stay ahead with the latest AI models the moment they drop - running faster, smoother, and fully private on your RTX-powered PC”
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
- [community] “Shocking! It's not like there weren't 4070 Ti Super cards that had 16GB GDDR6x at 21Gbps with 8448 cuda cores. 28Gbps with 8960 cuda cores?!…”
Radeon RX 9070 XTnone0/10The 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.
- [claimed-docs] “OS Support Windows 10 - 64-Bit Edition , Windows 11 - 64-Bit Edition , Linux x86 64-Bit”
- [claimed-docs] “AV1 Decode Yes AV1 Encode Yes”
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
ai-native userPerform bulk operations across many items at once
weight 2 · round drawnGeForce RTX 5070 Tinone0/10The 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.)
ai-native userSchedule recurring jobs or workflows
weight 2 · round drawnGeForce RTX 5070 Tinone0/10The 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.)
Creator media — stories about creator media in this arenaCreator media
Stories about creator media in this arena
Media engines
creatorHardware media engines and creator-app acceleration are documented — AV1/HEVC encoders, and professional or ISV-certified driver support where the vendor claims it
weight 2 · round to GeForce RTX 5070 TiNVIDIA'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.
- [claimed-docs] “Harness the power of the ninth-gen NVIDIA Encoder (NVENC) for blazing-fast video exports and AI-driven effects in DaVinci Resolve, Adobe Pre…”
- [claimed-docs] “GeForce RTX 50 Series GPUs unlock transformative performance in video editing, 3D rendering, and graphic design”
- [claimed-docs] “RTX Video Super Resolution and HDR uses AI to transform your videos in Chrome, Edge, or Firefox—automatically sharpening details and wiping …”
- [claimed-docs] “Remove distracting background noise, customize your background, and more at the touch of a button”
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.
- [claimed-docs] “AV1 Decode Yes AV1 Encode Yes”
Datacenter scale — stories about datacenter scale in this arenaDatacenter scale
Stories about datacenter scale in this arena
Scale out
ml engineerTrain and serve at datacenter scale on this part — documented high-bandwidth interconnect (NVLink, Infinity Fabric), multi-GPU systems, and rack-scale deployment
weight 3 · round drawnGeForce RTX 5070 Tinone0/10The 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.
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
- [claimed-docs] “CUDA Tile C++ is an expression of the CUDA Tile programming model in C++... allows you to write tile kernels in C++”
- [claimed-docs] “cuTile Python is an expression of the CUDA Tile programming model in Python... allows you to write tile kernels in Python”
- [claimed-docs] “NVIDIA Nsight Compute and Nsight System suite of tools designed to help developers optimize and increase performance of their applications”
Radeon RX 9070 XTnone0/10The 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.
- [claimed-docs] “The same ROCm™ stack that powers your desktop development on RDNA™ architecture GPUs also supports AMD Instinct™ accelerators on CDNA™ archi…”
- [claimed-docs] “This unified platform creates a seamless migration path, allowing you to develop applications locally and deploy them at scale with confiden…”
Driver openness — stories about driver openness in this arenaDriver openness
Stories about driver openness in this arena
Linux support
developerLinux is a first-class citizen for this GPU — documented Linux driver releases and independent Linux testing of this part
weight 2 · round to Radeon RX 9070 XTGeForce RTX 5070 Tinone0/10No 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.
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.
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “OS Support Windows 10 - 64-Bit Edition , Windows 11 - 64-Bit Edition , Linux x86 64-Bit”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “As a primarily open-source ecosystem, ROCm™ gives you the freedom to inspect, customize, and tailor the software stack to your specific need…”
Open drivers
developerRun this GPU on an open driver — open-source kernel modules or upstream Linux support documented by the vendor
weight 2 · round to Radeon RX 9070 XTGeForce RTX 5070 Tinone0/10NVIDIA 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.
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.
- [claimed-docs] “As a primarily open-source ecosystem, ROCm™ gives you the freedom to inspect, customize, and tailor the software stack to your specific need…”
- [claimed-docs] “OS Support Windows 10 - 64-Bit Edition , Windows 11 - 64-Bit Edition , Linux x86 64-Bit”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
Gaming performance — stories about gaming performance in this arenaGaming performance
Stories about gaming performance in this arena
4k gaming
gamerThis card drives high-refresh 4K gaming — vendor performance claims corroborated by independent game benchmarks
weight 3 · round to GeForce RTX 5070 TiNVIDIA'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.
- [claimed-docs] “new DLSS Multi Frame Generation boosts FPS by using AI to generate up to five frames per rendered frame”
- [claimed-docs] “Dynamically adjust your multiplier to maximize smoothness across different games and scenes on GeForce RTX 50 Series GPUs”
- [claimed-docs] “The NVIDIA Blackwell architecture unlocks the game-changing realism of path tracing. Experience cinematic quality visuals at unprecedented s…”
- [claimed-docs] “Reflex technologies optimize the graphics pipeline for ultimate responsiveness, providing faster target acquisition, quicker reaction times,…”
- [community] “The ASUS GeForce RTX 5070 Ti TUF OC comes with a fantastic all-metal cooling solution built like a tank. During testing, the card ran whispe…”
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.
- [community] “The RX 9070 XT offers competitive performance with similarly priced NVIDIA options in both raster and ray tracing, at a starting price of $6…”
Upscaling
gamerAI upscaling and frame generation are supported on this card — the DLSS or FSR generation is documented for this part, with broad game support
weight 2 · round to GeForce RTX 5070 TiNVIDIA'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).
- [claimed-docs] “new DLSS Multi Frame Generation boosts FPS by using AI to generate up to five frames per rendered frame”
- [claimed-docs] “DLSS replaces hand-tuned denoisers with an NVIDIA supercomputer-trained AI network that generates higher-quality pixels between sampled rays”
- [claimed-docs] “Boosts performance by using AI to output higher-resolution frames from a lower-resolution input”
- [claimed-docs] “DLAA uses the same Super Resolution technology developed for DLSS, constructing a more realistic, high-quality image at native resolution”
- [claimed-docs] “With the NVIDIA app you can update hundreds of games to use the latest DLSS features including Multi Frame Generation”
- [community] “The ASUS GeForce RTX 5070 Ti TUF OC comes with a fantastic all-metal cooling solution built like a tank. During testing, the card ran whispe…”
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.
- [claimed-docs] “AMD FSR™ "Redstone"”
- [community] “The RX 9070 XT offers competitive performance with similarly priced NVIDIA options in both raster and ray tracing, at a starting price of $6…”
Memory vram — stories about memory vram in this arenaMemory vram
Stories about memory vram in this arena
Llm memory
ai-native userRun a 70B-class quantized LLM on this GPU — published VRAM capacity and memory bandwidth that make local or single-node inference practical
weight 3 · round drawnGeForce RTX 5070 Tinone0/10Evidence 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.
- [community] “Shocking! It's not like there weren't 4070 Ti Super cards that had 16GB GDDR6x at 21Gbps with 8448 cuda cores. 28Gbps with 8960 cuda cores?!…”
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
- [claimed-docs] “Stay ahead with the latest AI models the moment they drop - running faster, smoother, and fully private on your RTX-powered PC”
Radeon RX 9070 XTnone0/10The 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.
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “A local workstation equipped with a Radeon™ GPU, featuring up to 48GB of VRAM, offers a secure and economical alternative to relying solely …”
Memory spec
ml engineerMemory specs are published in full for this exact part — capacity, memory type, bus width, and bandwidth
weight 2 · round drawnGeForce RTX 5070 Tinone0/10The 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.
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
ai-native userRead the product's source under an open license
weight 2 · round to Radeon RX 9070 XTGeForce RTX 5070 Tinone0/10The 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.)
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.
- [claimed-docs] “As a primarily open-source ecosystem, ROCm™ gives you the freedom to inspect, customize, and tailor the software stack to your specific need…”
- [probe] “PROBE llms.txt: HTTP 200 at https://rocm.docs.amd.com/llms.txt # ROCm documentation > Note: ROCm documentation is split across multiple pro…”
ai-native userSelf-host the core product
weight 3 · round to Radeon RX 9070 XTGeForce RTX 5070 Tinone0/10The 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.)
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.
- [claimed-docs] “A local workstation equipped with a Radeon™ GPU, featuring up to 48GB of VRAM, offers a secure and economical alternative to relying solely …”
- [claimed-docs] “This unified platform creates a seamless migration path, allowing you to develop applications locally and deploy them at scale with confiden…”
- [claimed-docs] “The same ROCm™ stack that powers your desktop development on RDNA™ architecture GPUs also supports AMD Instinct™ accelerators on CDNA™ archi…”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
Power cooling — stories about power cooling in this arenaPower cooling
Stories about power cooling in this arena
Efficiency
ml engineerSustained workloads are power-efficient on this part — documented power envelopes with independent performance-per-watt testing
weight 2 · round drawnGeForce RTX 5070 Tinone0/10Evidence 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.
- [community] “The ASUS GeForce RTX 5070 Ti TUF OC comes with a fantastic all-metal cooling solution built like a tank. During testing, the card ran whispe…”
Radeon RX 9070 XTnone0/10Evidence 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.
Psu planning
gamerSpec a build around published board power — TDP/TGP, connector requirements, and cooling guidance for this exact card
weight 2 · round drawnGeForce RTX 5070 Tinone0/10The 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.
- [community] “The ASUS GeForce RTX 5070 Ti TUF OC comes with a fantastic all-metal cooling solution built like a tank. During testing, the card ran whispe…”
Radeon RX 9070 XTnone0/10The 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.
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
ai-native userPrevent my data from being used to train AI models
weight 3 · round to GeForce RTX 5070 TiNVIDIA 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.
- [claimed-docs] “Stay ahead with the latest AI models the moment they drop - running faster, smoother, and fully private on your RTX-powered PC”
- [claimed-docs] “Generate incredible images and videos, tap into optimized ComfyUI workflows, and run the latest AI models locally in seconds”
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
Software toolchain — stories about software toolchain in this arenaSoftware toolchain
Stories about software toolchain in this arena
Compute stack
developerShip GPU-compute workloads on the vendor's toolchain — CUDA or ROCm/HIP documentation lists this part as a supported target
weight 3 · round to Radeon RX 9070 XTThe 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.
- [claimed-docs] “CUDA Tile C++ is an expression of the CUDA Tile programming model in C++... allows you to write tile kernels in C++”
- [claimed-docs] “cuTile Python is an expression of the CUDA Tile programming model in Python... allows you to write tile kernels in Python”
- [claimed-docs] “NVIDIA Nsight Compute and Nsight System suite of tools designed to help developers optimize and increase performance of their applications”
- [probe] “PROBE docs-md: HTTP 404 at https://developer.nvidia.com/cuda-toolkit.md”
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.
- [claimed-docs] “PyTorch on Windows updated with ROCm 7.2.1 on AMD Radeon graphics products and AMD Ryzen AI processors.”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Windows® PyTorch”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “The same ROCm™ stack that powers your desktop development on RDNA™ architecture GPUs also supports AMD Instinct™ accelerators on CDNA™ archi…”
Frameworks
ml engineerPyTorch and mainstream ML frameworks run on this GPU through officially documented builds and support matrices
weight 2 · round to Radeon RX 9070 XTNVIDIA'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.
- [claimed-docs] “Experiment, build, and optimize with the latest AI technologies on RTX AI PCs. Access curated, GPU-optimized SDKs and models, and maximize p…”
- [claimed-docs] “CUDA Tile C++ is an expression of the CUDA Tile programming model in C++... allows you to write tile kernels in C++”
- [claimed-docs] “cuTile Python is an expression of the CUDA Tile programming model in Python... allows you to write tile kernels in Python”
- [claimed-docs] “NVIDIA Nsight Compute and Nsight System suite of tools designed to help developers optimize and increase performance of their applications”
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.
- [claimed-docs] “PyTorch on Windows updated with ROCm 7.2.1 on AMD Radeon graphics products and AMD Ryzen AI processors.”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Linux® PyTorch, TensorFlow, JAX, ONNX”
- [claimed-docs] “Radeon™ GPUs (9000 & select 7000 Series) Windows® PyTorch”
- [claimed-docs] “**vLLM**: Full support.”
- [claimed-docs] “**Llama.cpp**: Supported for efficient inference.”
- [claimed-docs] “OS Support Windows 10 - 64-Bit Edition , Windows 11 - 64-Bit Edition , Linux x86 64-Bit”
Not comparable on these axes
ai-native userPlug MCP servers into this product so it can use their tools
weight 3 · not comparableGeForce RTX 5070 Tin/aA GPU is hardware; MCP server integration is an agent/software-tooling concept that does not apply to a graphics card product category.
ai-native userConnect an agent via an official MCP server
weight 3 · not comparableGeForce RTX 5070 Tin/aA 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.
ai-native userIssue scoped/least-privilege API credentials for an agent
weight 2 · not comparableGeForce RTX 5070 Tin/aA 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.
ai-native userSubscribe to events via webhooks
weight 2 · not comparableGeForce RTX 5070 Tin/aA GPU hardware product has no concept of webhook event subscriptions; this axis is a category error for this product type.
ai-native userSet up automations that run autonomously in the background
weight 2 · not comparableGeForce RTX 5070 Tin/aThe 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.
Radeon RX 9070 XTn/aThis 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.
ai-native userExplore an interactive API reference with runnable examples
weight 2 · not comparableGeForce RTX 5070 Tin/aThis 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.
Radeon RX 9070 XTn/aThe 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).
ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)
weight 2 · not comparableGeForce RTX 5070 Tin/aThe 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.
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.nvidia.com/openapi.json, https://developer.nvidia.com/swagger.json, https://develo…”
ai-native userTest against a sandbox environment without touching production data
weight 1 · not comparableGeForce RTX 5070 Tin/aA 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.
ai-native userRely on versioned APIs with a documented deprecation policy
weight 2 · not comparableGeForce RTX 5070 Tin/aThe 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.
ai-native userDefine rules that trigger actions automatically on events
weight 3 · not comparableGeForce RTX 5070 Tin/aA 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.
ai-native userVersion, review, and roll back my automations
weight 1 · not comparableGeForce RTX 5070 Tin/aThis 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.
ai-native userDo everything through the API that I can do in the UI
weight 2 · not comparableGeForce RTX 5070 Tin/aThe 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.
ai-native userExport all of my data in open formats and leave
weight 3 · not comparableGeForce RTX 5070 Tin/aThe 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.
ai-native userChoose where my data is stored (region/residency)
weight 2 · not comparableGeForce RTX 5070 Tin/aThis 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.
ai-native userControl data retention and deletion
weight 2 · not comparableGeForce RTX 5070 Tin/aThis 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.
ai-native userOpt out of telemetry and usage tracking
weight 2 · not comparableGeForce RTX 5070 Tin/aA 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.