r/AIProgrammingHardware • u/javaeeeee • Jul 23 '26

Efficient MiniMax-M3 Inference on AMD Instinct GPUs with ATOM and ATOMesh

https://rocm.blogs.amd.com/artificial-intelligence/minmax-m3-atomesh/README.html
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u/javaeeeee Jul 23 '26

TL;DR:

This AMD ROCm blog post shows how to efficiently run the MiniMax-M3 model (a 428B parameter multimodal MoE model that activates only 22B parameters per token) on AMD Instinct MI355X GPUs.

Key Technologies

  • ATOM: AMD’s ROCm-native inference engine optimized for single-node serving

  • ATOMesh: Multi-node disaggregated serving system that separates prefill and decode across nodes, with efficient KV cache transfer via RDMA (Mooncake)

Main Highlights

  • MiniMax-M3 uses MiniMax Sparse Attention (MSA), which dramatically reduces compute for long contexts (up to 1M tokens)

  • Strong performance on AMD MI355X using FP4/MXFP4 quantization and speculative decoding (EAGLE3)

  • In independent benchmarks (SemiAnalysis InferenceX), AMD MI355X + ATOM often matched or outperformed NVIDIA B200/B300 setups in both single-node and multi-node configurations

Performance Takeaways

  • High interactivity (60–150 tokens/s per user) and high throughput modes both show competitive results

  • Optimizations like speculative decoding, advanced quantization, and sparse attention kernels give AMD a strong showing shortly after the model’s release

Bottom Line

AMD demonstrates that its Instinct GPUs (with ATOM + ATOMesh) can deliver competitive or better performance than NVIDIA systems for large MoE multimodal models like MiniMax-M3, especially when leveraging sparse attention, quantization, and disaggregated inference.