Scaling MiniMax-M3 Inference with Distributed Serving and Operator Co-Design on AMD Instinct MI355X GPUs

2026-07-21 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

The article details optimizations for MiniMax-M3 inference on AMD Instinct™ MI355X GPUs, achieved through a combination of ATOM, AITER, and ATOMesh frameworks. Published on July 21, 2026, the work addresses system-level bottlenecks in quantization, attention, KV cache management, speculative decoding, and disaggregated inference. Key improvements include ATOM online quantization for faster attention GEMMs, AITER sparse attention with FP8 KV cache and page-16 SHUFFLE layout, EAGLE3 speculative decoding to reduce target-model forward passes, and ATOMesh P/D disaggregation with MiniMax-M3 sparse indexer-key cache transfer. These coordinated efforts enhance serving throughput, reduce token latency, and maintain accuracy for MiniMax-M3 workloads, demonstrating significant performance gains in FP4 serving mode.

Key takeaway

For MLOps Engineers deploying MiniMax-M3 on AMD Instinct MI355X GPUs, integrating ATOM, AITER, and ATOMesh is crucial for maximizing inference performance. You should adopt online quantization for attention GEMMs and leverage AITER's sparse attention with FP8 KV cache. Implement EAGLE3 speculative decoding to reduce target model workload and utilize ATOMesh for prefill/decode disaggregation, ensuring efficient resource allocation and improved throughput. This holistic approach significantly reduces token latency and boosts serving efficiency.

Key insights

Optimizing complex LLMs like MiniMax-M3 on AMD GPUs requires co-design across quantization, attention, speculative decoding, and distributed serving.

Principles

• Full inference path optimization yields higher serving efficiency.
• Online quantization improves GEMM throughput selectively.
• Disaggregated inference allows independent resource allocation.

Method

The approach combines ATOM online quantization, AITER sparse attention with FP8 KV cache, EAGLE3 speculative decoding, and ATOMesh P/D disaggregation with sparse indexer-key cache transfer.

In practice

• Use ATOM for online FP8/FP4 quantization.
• Implement AITER for sparse attention and FP8 KV cache.
• Deploy ATOMesh for P/D disaggregation.

Topics

• MiniMax-M3
• LLM Inference Optimization
• AMD Instinct MI355X
• Speculative Decoding
• Distributed LLM Serving
• Quantization

Code references

• ROCm/ATOM
• ROCm/aiter

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.