Deploy Long-Context Reasoning and Agentic Workflows with MiniMax M3 on NVIDIA Accelerated Infrastructure | NVIDIA Technical Blog

What this post added

This post details the deployment of the MiniMax M3 model, a 428B parameter Mixture-of-Experts model with 1M-token context and native multimodality, on NVIDIA Blackwell accelerated infrastructure. It highlights the MiniMax Sparse Attention (MSA) mechanism, which replaces standard quadratic attention with a pre-filtering stage for efficient long-context reasoning and agentic workflows, achieving significant speedups in prefill and decoding. The post also outlines deployment strategies using open-source inference engines (TensorRT LLM, SGLang, vLLM), large-scale serving with NVIDIA Dynamo, and customization with the NVIDIA NeMo Framework, including support for N-D parallelism and context parallelism up to 128k tokens.