MiniMax M2.7 Advances Scalable Agentic Workflows on NVIDIA Platforms for Complex AI Applications

MiniMax M2.7 is a sparse Mixture-of-Experts (MoE) language model with 230B total parameters and only 10B active per token, featuring a 200K context window. NVIDIA details how to deploy it using vLLM and SGLang with specific inference optimizations — a fused QK RMS Norm kernel and FP8 MoE kernel — that deliver up to 2.7x throughput improvements on NVIDIA Blackwell Ultra GPUs. The post also covers building long-running agents via NVIDIA NemoClaw and OpenShell, fine-tuning with the NeMo AutoModel library and NeMo RL, and accessing the model through NVIDIA NIM microservices or free endpoints on build.nvidia.com.