[AMD][MI355X] Bump MiniMax-M3 AgentX to latest ROCm nightly - #2959
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Reviewed by Cursor Bugbot for commit 8da0f81. Configure here.
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34461269863 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34461553099 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34462294916 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34527361800 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34537533772 |
1 similar comment
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34537533772 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34537533772 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34609443618 |
1 similar comment
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34609443618 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34701797970 |
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/stage-results 34609443618 |
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@chunfangamd staged run 34609443618: https://inferencemax-app-git-staging-semianalysisai.vercel.app/inference?i_dates=2026-09-11~r34609443618 This run remains available across future |
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/reuse-sweep-run 34609443618 |
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As a PR reviewer and CODEOWNER, I have reviewed this and have:
- Verified that as of the moment of typing this, this is the latest version of PR_REVIEW_CHECKLIST.md
- Verified that the general code quality meets the InferenceX standard and does not make the code quality any worse.
- Verified that this PR has passed PR validation. Please link to GitHub Action workflow that shows this.
- Verified that this PR passes evals. Please link to GitHub Action workflow that shows this.
- Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
- For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance, with the acceptance-length value taken from the committed golden AL curve in golden_al_distribution/ for that model, thinking mode, and draft length. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
- Verified against the current MODELS.md that this PR does not submit a deprecated model, scenario, or model-scenario combination.
- Verified that the model architecture isn't changed with benchmark hacks like using --hf-overrides to skipping indexer for every x layers on models that don't natively support this. As a general rule, we won't accept optimizations that reduces the number of model architecture FLOPs. Anything that makes that same computation run faster is fair game; FLOPs at lower precisions is fine, given that the config passes private evals. As an general north star princple, we should only use optimizations which is used in production by customers that care about accuracy
- If an company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM submission made using upstream https://hub.docker.com/u/vllm docker repo, upstream SGLang https://hub.docker.com/u/lmsysorg docker repo. The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet as supported by vLLM/SGLang community maintainers
- If an company claims that they support vLLM/SGLang as first class upstream in-tree LLM inference engines on their hardware, I have have verified that the respective vLLM/SGLang submission has been made before additional frameworks (TRT-LLM, ATOM, etc.). The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet.
- Verified that every single-node vLLM/SGLang recipe in this PR is documented in the official vLLM recipes and/or the SGLang cookbook:
- I linked the corresponding upstream PR in the vLLM recipe repo or SGLang repo and verified that it is MERGED before this InferenceX PR merges. An opened, draft, or closed-without-merge upstream PR does not satisfy this requirement. If the matching recipe was already published, I linked the published recipe/cookbook page in the additional detail section below.
- Verified that this PR does not patch the inference engine or serving stack — the pinned image must run as shipped. This covers .patch files / git apply / patch, inline patches embedded in benchmark scripts (e.g. a python3/sed heredoc that rewrites installed engine sources before serving), in-place edits of site-packages, monkey-patching, overwriting container files, and installing forked/rebuilt engine wheels on top of the pinned image. The only exception is a patch covered by a filled-out waiver at docs/waiver/
<PR_NUMBER>.md— named after the PR that introduces the patch and filed in that same PR, stating what is patched, why the unmodified upstream image cannot run this benchmark, the upstream PR/issue link, and the removal plan — which I have linked below in the additional detail section. - If this PR uses
append-only: true, verified that it only adds generated points or recipe variants inside a selected existing config/scenario and existing same-image visual curve: every previously generated point remains present with the same recipe, no prior point is removed or rerun, and every benchmark-affecting change in the complete diff can affect only the corresponding newly appended points (never an existing point), regardless of which file contains it. - If any of the above criteria cannot reasonably be satisfied, I have provided additional reasoning below.
Additional detail section:
- insert any additional info here
recipe is at https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3?hardware=mi355x&features=tool_calling%2Creasoning%2Cspec_decoding&variant=mxfp4
Signed: seungrokj
✅✅✅ Verdict: PASS ✅✅✅✅ Check 0 (CODEOWNER): PASS — Note (non-blocking): the changelog description says |
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@Oseltamivir @adibarra can you plz review this ? |
Note
Medium Risk
Changes benchmark serving flags (KV layout, FP8 indexer, higher GPU memory use, CUDA graph capture) and narrows the official concurrency matrix, which can shift measured AgentX curves and stability on TP2.
Overview
Bumps the MI355X MiniMax-M3 AgentX vLLM+MTP recipe to a newer immutable ROCm nightly and retunes serving for the optimized FP8 AITER indexer path.
The launch script always enables
VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT(drops the old TP2 vs TP4 branch), raises--gpu-memory-utilizationto 0.90, adds a hand-tunedcudagraph_capture_sizeslist underFULL_DECODE_ONLY, and passes--attention-configwithindexer_kv_dtype: fp8. The amd-master config pins the new image digest and trims agentic concurrency sweeps (fewer points on TP4; TP2 capped at conc 1–2). perf-changelog.yaml records the image bump and optimization notes.Reviewed by Cursor Bugbot for commit 4bcd19a. Bugbot is set up for automated code reviews on this repo. Configure here.