MiniMax-M3

Headlines · 3

1. 6d
   MiniMax releases MXFP8-quantized MiniMax-M3

   MiniMax’s model card lists MiniMax-M3-MXFP8 as an MXFP8-quantized variant of MiniMax-M3, tagged for image-text-to-text tasks. It describes M3 as a native multimodal model with a 1M-token context, about 428B total parameters and about 23B activated parameters, trained from the first step across text, image and video. The model uses MiniMax Sparse Attention, which MiniMax says is designed for million-token contexts and reduces attention compute and memory use compared with GQA. MiniMax also reports 9× prefill and 15× decode speedups over M2 at a 1M-token context, with per-token compute reduced to one-twentieth; these figures are the company’s claims.

   MiniMax · Hugging Face · 🔥 0
2. 6d
   MiniMax releases M3 native multimodal model with 1M-token context

   MiniMax published MiniMax-M3 on Hugging Face, describing it as a native multimodal model with a 1M-token context, approximately 428B total parameters and 23B activated parameters. The model card says mixed-modality training combines text, image and video semantics, while MiniMax Sparse Attention targets more efficient million-token inference. MiniMax also claims 9× prefill and 15× decode speedups over M2 at 1M context, but these performance and efficiency figures have not been independently verified.

   MiniMax · Hugging Face · 🔥 0
3. Oct 6
   vLLM v0.31.0 expands serving, speculation, and model support

   vLLM v0.31.0 adds broad serving and model-support updates, including DeepSeek-V4.1-Flash optimizations, Model Runner V2 speculative decoding, larger-scale expert parallelism, and improved multimodal support. It also introduces the `vllm preload` weight-cache daemon for faster engine restarts and experimental CRIU-based initialized-engine snapshots, alongside several security and breaking changes.

   vLLM Releases · 🔥 0

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