Following the Bottleneck: Optimizing MiniMax M3 on AMD Instinct MI355X

The MiniMax M3 day-0 post described the first working vLLM implementation: MiniMax Sparse Attention (MSA), multimodal inputs, reasoning and tool outputs, MXFP8 weights, and EAGLE3 on AMD Instinct MI355X.

This follow-up is about what happened after the model ran. The useful result is not only a higher throughput number. It is a way to decide what to optimize next when the bottleneck keeps moving.

Results in one minute

Public results from the SemiAnalysis InferenceX benchmark show what MiniMax-M3 now delivers on AMD Instinct MI355X:

• At concurrency 32, fixed-topology MXFP8 standard serving rose from 109.1 to 342.4 output tokens/s/GPU, or 3.14× the day-0 result. Median TTFT fell from 1.46 to 0.67 seconds, and mean TPOT fell from 69.1 to 22.1 milliseconds.
• At concurrency 128, the same TP4/EP1 four-GPU path rose from 297.8 to 623.7 output tokens/s/GPU, or 2.09×. Median TTFT fell from 3.53 to 1.54 seconds, and mean TPOT fell from 100.7 to 48.8 milliseconds.
• MXFP4 first rose from 212.1 to 716.8 output tokens/s/GPU at concurrency 128 under the same TP4/EP1 four-GPU contract. A later TP2/EP1 result reached 943.5 output tokens/s/GPU, 31.6% above that TP4 checkpoint and 4.45× the initial per-GPU…