AMD Instinct MI355X

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Build against official SDKs G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Run the product headlessly / in CI for automation G

Agent access

Use an official CLI G

Agent access

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Rely on versioned APIs with a documented deprecation policy G

Api quality

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Issue scoped/least-privilege API credentials for an agent G

Agent access

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

Subscribe to events via webhooks G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Self-host the core product G

Ship GPU-compute workloads on the vendor's toolchain — CUDA or ROCm/HIP documentation lists this part as a supported target C

Compute stack

Run a 70B-class quantized LLM on this GPU — published VRAM capacity and memory bandwidth that make local or single-node inference practical C

Llm memory

This 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 C

Inference stack

Train and serve at datacenter scale on this part — documented high-bandwidth interconnect (NVLink, Infinity Fabric), multi-GPU systems, and rack-scale deployment C

Scale out

Size training and inference from published tensor throughput — TFLOPS or TOPS with precision and sparsity stated, not a bare marketing number C

Tensor specs

Define rules that trigger actions automatically on events G

Export all of my data in open formats and leave G

Prevent my data from being used to train AI models G

This card drives high-refresh 4K gaming — vendor performance claims corroborated by independent game benchmarks C

4k gaming

PyTorch and mainstream ML frameworks run on this GPU through officially documented builds and support matrices C

Frameworks

Linux is a first-class citizen for this GPU — documented Linux driver releases and independent Linux testing of this part C

Linux support

Memory specs are published in full for this exact part — capacity, memory type, bus width, and bandwidth C

Memory spec

Perform bulk operations across many items at once G

Schedule recurring jobs or workflows G

Sustained workloads are power-efficient on this part — documented power envelopes with independent performance-per-watt testing C

Efficiency

AI upscaling and frame generation are supported on this card — the DLSS or FSR generation is documented for this part, with broad game support C

Upscaling

Choose where my data is stored (region/residency) G

Control data retention and deletion G

Do everything through the API that I can do in the UI G

Hardware media engines and creator-app acceleration are documented — AV1/HEVC encoders, and professional or ISV-certified driver support where the vendor claims it C

Media engines

Opt out of telemetry and usage tracking G

Read the product's source under an open license G

Run this GPU on an open driver — open-source kernel modules or upstream Linux support documented by the vendor C

Open drivers

Spec a build around published board power — TDP/TGP, connector requirements, and cooling guidance for this exact card C

Psu planning

Version, review, and roll back my automations G