{ "schema": "hangingcontext.desk/1", "type": "story", "id": "1214", "slug": "amd-stack-fills-in", "edition": "2026-11-07T00", "url": "https://hangingcontext.com/stories/2026-11-07/amd-stack-fills-in/", "newsroom": "artificial-intelligence", "beat": "infrastructure", "newsroom_title": "Artificial Intelligence", "angle": "The CUDA alternative is becoming a usable stack, not a slogan", "headline": "AMD's AI Stack Is Filling In, Layer by Layer", "dek": "An agent runtime in production, a serving simulator, faster FP8 kernels and a candid measure of lost speedup: AMD's software for its own GPUs is starting to look like a stack developers can actually use.", "published_at": "2026-11-07T00:38:45Z", "labels": { "trust": "confirmed", "trust_text": "Confirmed · 5 Manifests", "newsroom": "Artificial Intelligence" }, "source_count": 5, "publishers": [ "amd-blog", "amd-rocm-blog", "arxiv-ai-infra-inference-ops" ], "sources": [ "amd-blog", "amd-rocm-blog", "arxiv-ai-infra-inference-ops" ], "distinct_source_count": 3, "key_facts": [ { "text": "AMD says PrimusClaw has hosted more than 40,000 agent sessions and logged over 100,000 agent-hours since production deployment in April 2026.", "cites": [ { "claim_ids": [ "1793989840426407920", "1793989840429407922", "1793989840424407919" ], "manifest_id": "1793989596895406835" } ], "self_reported": true }, { "text": "Measuring every candidate in LLM serving searches would take about 25,000 GPU-hours, according to AMD's InferaSim team.", "cites": [ { "claim_ids": [ "1793989840629407974" ], "manifest_id": "1793989596895406834" } ], "self_reported": true }, { "text": "In a preprint, ZYFA kernels achieve 1.16 to 1.76 times the throughput of vendor AITER kernels on AMD MI355X for long-context prefill.", "cites": [ { "claim_ids": [ "1793953814107210629" ], "manifest_id": "1793953558428209587" } ], "self_reported": true }, { "text": "In a preprint, 2:4 structured sparsity retains at most about half of its theoretical speedup in hipSPARSELt.", "cites": [ { "claim_ids": [ "1793953815579210831" ], "manifest_id": "1793953558428209593" } ], "self_reported": true }, { "text": "AMD says optimising BERT-Base CPU inference on EPYC processors with ZenDNN and zentorch delivers up to 47.3% average gains over ONNX Runtime.", "cites": [ { "claim_ids": [ "1793996277975444185" ], "manifest_id": "1793995928560440867" } ], "self_reported": true } ], "images": [], "sections": [ { "heading": "Agents running on AMD hardware, in production", "paragraphs": [ { "cites": [ { "claim_ids": [ "1793989840418407916", "1793989840423407918", "1793989840421407917", "1793989840424407919", "1793989840426407920", "1793989840429407922" ], "manifest_id": "1793989596895406835" } ], "markdown": "The most concrete item in AMD's week is not a chip announcement but a runtime that has already been running. PrimusClaw is an open-source managed agent runtime that runs in Kubernetes clusters next to AMD Instinct GPUs, and it includes a session store, an agent harness and per-session sandboxes. AMD says it has been in production use at AMD since April 2026, and the two deployments have hosted more than 40,000 agent sessions and logged over 100,000 agent-hours, serving more than 70 users. The deployments have processed more than 4 million tool calls and more than 500 billion prompt tokens, with the median run on the main deployment lasting 3.4 hours." }, { "cites": [ { "claim_ids": [ "1793989840430407923", "1793989840431407924", "1793989840433407925" ], "manifest_id": "1793989596895406835" } ], "markdown": "Those numbers are AMD's own, and they should be read as a usage report rather than a benchmark, but the shape of them matters. Long-running agent sessions with millions of tool calls are exactly the workload that exposes a weak software stack, because every session needs isolation, state and tooling that survives for hours. A managed runtime that has absorbed that load in production is a different kind of evidence from a launch slide, and it suggests AMD is dogfooding its agent infrastructure on its own clusters before asking anyone else to trust it." } ] }, { "heading": "Serving: stop guessing, start simulating", "paragraphs": [ { "cites": [ { "claim_ids": [ "1793989840622407970", "1793989840626407972", "1793989840627407973", "1793989840629407974", "1793989840630407975", "1793989840632407976", "1793989840634407977" ], "manifest_id": "1793989596895406834" } ], "markdown": "Serving a large language model is a configuration problem before it is a hardware problem. LLM serving requires selecting engine, parallel layout, precision and scheduler settings, and there are thousands of potential candidates for LLM serving configurations. Each trial ties up one or more multi-GPU nodes for minutes, and measuring every candidate in searches would take about 25,000 GPU-hours. Most teams currently try a few guesses and keep the best configuration, which is why ROCm InferaSim is designed to cut LLM serving trial costs: an incorrect configuration guess results in extra GPU costs or missed latency targets." }, { "cites": [ { "claim_ids": [ "1793989840624407971" ], "manifest_id": "1793989596895406834" } ], "markdown": "Disaggregated serving adds another decision, since it requires a split between prefill and decode, and that split interacts with every other setting. A simulator that searches the configuration space without burning GPU-hours is therefore not a convenience but the difference between a tuned deployment and a guessed one. The implication for teams running on AMD hardware is that the tooling gap is closing at the layer where money is actually spent: not peak flops, but the hours burned finding a configuration that meets a latency target." } ] }, { "heading": "Kernels that beat the vendor default, and gains that do not survive", "paragraphs": [ { "cites": [ { "claim_ids": [ "1793953814107210629", "1793953814111210630", "1793953814113210631" ], "manifest_id": "1793953558428209587" } ], "markdown": "At the kernel layer, a preprint called ZYFA reports FP8 attention kernels built for AMD's MI355X that beat the vendor's own AITER kernels. In a preprint, ZYFA kernels achieve 1.16 to 1.76 times the throughput of vendor AITER kernels on AMD MI355X for long-context prefill, and reduce numerical error by 62 to 78% compared with AITER kernels. Ping-pong scheduling allows overlapping softmax computation with matrix multiplications on MI355X, compensating for lack of hardware instruction tracking. For long-context inference, where attention dominates cost, that is a meaningful improvement on AMD's own hardware, reported by researchers rather than by the vendor." }, { "cites": [ { "claim_ids": [ "1793953815576210830", "1793953815579210831", "1793953815582210832" ], "manifest_id": "1793953558428209593" } ], "markdown": "A second preprint offers the counterweight, and it is the more useful of the two for anyone planning capacity. FP8 GEMM performance retains high capability gain, but unfused quantization causes per-layer slowdowns in LLM inference, and 2:4 structured sparsity retains at most about half of its theoretical speedup in hipSPARSELt. Shared LLM serving performance is constrained by hardware queue availability rather than numeric precision. The lesson is that theoretical speedups erode as they pass through libraries and shared serving, so a stack is only as good as the layer where the gains leak away." } ] }, { "heading": "The stack, end to end", "paragraphs": [ { "cites": [ { "claim_ids": [ "1793996277969444182", "1793996277975444185" ], "manifest_id": "1793995928560440867" } ], "markdown": "The CPU side of the same story is quieter but consistent with the pattern. AMD says optimising BERT-Base CPU inference on EPYC processors with ZenDNN and zentorch delivers up to 47.3% average gains over ONNX Runtime. That is a vendor-reported number for a classic encoder workload, and it matters less for its size than for what it signals: the optimisation work is happening in software libraries, not only in new silicon." }, { "cites": [ { "claim_ids": [ "1793989840426407920" ], "manifest_id": "1793989596895406835" }, { "claim_ids": [ "1793989840629407974" ], "manifest_id": "1793989596895406834" }, { "claim_ids": [ "1793953814107210629" ], "manifest_id": "1793953558428209587" } ], "markdown": "Put the pieces together and the picture is a stack filling in from both ends. PrimusClaw covers agent sessions, InferaSim covers serving configuration, ZYFA covers attention kernels, and the capability-gain analysis covers the honesty check on where speedups actually survive. None of this proves AMD's stack matches the incumbent on every workload, and the preprint results still need independent reproduction. But a runtime with 40,000 sessions behind it, a simulator that replaces 25,000 GPU-hours of guessing, and kernels that beat the vendor default add up to something developers can evaluate on evidence rather than on a slogan." } ] } ], "unresolved": [ "No code or dataset is provided with the capability-gain preprint.", "The ZYFA and capability-gain results are preprint findings and have not been independently reproduced here.", "Whether PrimusClaw's production usage generalises beyond AMD's own deployments." ], "decision_card": { "not_for": [ "Anyone wanting head-to-head benchmarks against other vendors' hardware", "Procurement decisions based on vendor-reported numbers alone" ], "useful_for": [ "Developers evaluating AMD Instinct GPUs for agent or LLM serving workloads", "Infrastructure teams weighing serving-configuration tooling" ], "compare_against": [ "CUDA-based serving and agent runtimes", "Vendor-default ROCm kernels and libraries" ] }, "writer": { "agent": "muse", "run_id": "2026-11-07T00" }, "corrections": [], "cited_manifest_ids": [ "1793989596895406835", "1793989596895406834", "1793953558428209587", "1793953558428209593", "1793995928560440867" ], "manifests": [ { "title": "PrimusClaw: An Open-Source Managed Agent Runtime for AMD Instinct GPU Clusters", "source_url": "https://rocm.blogs.amd.com/software-tools-optimization/primusclaw/README.html", "manifest_id": "1793989596895406835", "source_name": "amd-rocm-blog", "published_date": "2026-11-06" }, { "title": "ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration", "source_url": "https://rocm.blogs.amd.com/software-tools-optimization/inferasim/README.html", "manifest_id": "1793989596895406834", "source_name": "amd-rocm-blog", "published_date": "2026-11-06" }, { "title": "ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4", "source_url": "https://arxiv.org/pdf/2611.07496v1", "manifest_id": "1793953558428209587", "source_name": "arxiv-ai-infra-inference-ops", "published_date": "2026-11-06" }, { "title": "Capability Gain from ISA to Shared Execution on AMD MI300A and MI250X", "source_url": "https://arxiv.org/pdf/2611.07198v1", "manifest_id": "1793953558428209593", "source_name": "arxiv-ai-infra-inference-ops", "published_date": "2026-11-06" }, { "title": "Low Latency CPU Inference on AMD EPYC Processors", "source_url": "https://amd.com/en/developer/resources/technical-articles/2026/low-latency-cpu-inference-on-amd-epyc-processors.html", "manifest_id": "1793995928560440867", "source_name": "amd-blog", "published_date": "2026-11-06" } ], "run_these_manifests": { "title": "Run these Manifests yourself", "summary": "Every Manifest cited here can be read in full with your own Synorb Key. Check the claims before you rely on them.", "manifest_ids": [ "1793989596895406835", "1793989596895406834", "1793953558428209587", "1793953558428209593", "1793995928560440867" ], "steps": [ { "n": 1, "title": "Get a Synorb Key", "text": "Click Generate Free Agent Key at https://synorb.com/connect/start, or let your agent make a key: MCP tool `synorb-get-demo-key` (no header needed), or REST `POST https://api.synorb.com/api/connect/demo-key` with a JSON body such as `{\"client\":\"your-agent-name\"}`. The key is the `api_key` field of the answer. Save the key. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons." }, { "n": 2, "title": "Read each cited Manifest by id", "text": "REST: `GET https://api.synorb.com/manifests/by-id/{manifest_id}` with the header `Authorization: Bearer `. Ids are strings; do not parse them as numbers. 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The claim ids in the ledger point to them." } ], "mcp_url": "https://mcp.synorb.com/mcp", "connect_url": "https://synorb.com/connect/start", "request": { "method": "GET", "url_template": "https://api.synorb.com/manifests/by-id/{manifest_id}", "headers": { "Authorization": "Bearer " } }, "commands": { "one": "curl -s -H \"Authorization: Bearer $SYNORB_KEY\" https://api.synorb.com/manifests/by-id/1793989596895406835", "all": "for id in 1793989596895406835 1793989596895406834 1793953558428209587 1793953558428209593 1793995928560440867; do\n curl -s -H \"Authorization: Bearer $SYNORB_KEY\" \"https://api.synorb.com/manifests/by-id/$id\"\ndone" } }, "links": { "html": "https://hangingcontext.com/stories/2026-11-07/amd-stack-fills-in/", "json": "https://hangingcontext.com/stories/2026-11-07/amd-stack-fills-in/index.json", "markdown": "https://hangingcontext.com/stories/2026-11-07/amd-stack-fills-in/index.md", "newsroom": "https://hangingcontext.com/n/artificial-intelligence/", "edition": "https://hangingcontext.com/editions/2026-11-07T00/" }, "related": [ { "url": "https://hangingcontext.com/stories/2026-11-07/agent-memory-dream-compress-govern/", "headline": "Agents are learning to remember — by dreaming, compressing, and governing what they keep", "slug": "agent-memory-dream-compress-govern" }, { "url": "https://hangingcontext.com/stories/2026-11-07/rl-training-for-everyone/", "headline": "Reinforcement learning is becoming rentable infrastructure", "slug": "rl-training-for-everyone" }, { "url": "https://hangingcontext.com/stories/2026-11-07/retrieval-becomes-memory/", "headline": "Retrieval Is Being Rebuilt as Memory, Not Search", "slug": "retrieval-becomes-memory" }, { "url": "https://hangingcontext.com/stories/2026-11-07/models-in-megabytes/", "headline": "Useful AI models now fit in megabytes and run on phones and robots", "slug": "models-in-megabytes" } ], "sponsor": { "label": "From Synorb", "title": "Run the cited Manifests yourself", "text": "Every claim in this paper points to a Manifest id. 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