Experimental int4 w4a16, I have not been able to test it as the vLLM M3's support PR does not support pipeline paralelism and I don't have the hardware to test tensor paralelism, so here may be dragons, but people like to tinker.
You will need this PR from vllm to make it work
https://github.com/vllm-project/vllm/pull/45381
This is using RTN quantization, not full calibrated Auto-round.
MiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters.
M3 is powered by
MiniMax Sparse Attention (MSA), a high-performance sparse attention operator designed for million-token contexts. Compared with GQA, MSA dramatically reduces the attention compute and memory footprint while preserving model quality.
We recommend the following inference frameworks (listed alphabetically) to serve the model: