Serving MiniMax-M3 for efficient inference: Unlocking 1M-Token Context and Multimodality Without Regrets

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This post details the engineering breakthroughs made by Together AI to efficiently serve the MiniMax M3 model, focusing on optimizations for its 1M-token context window and native multimodality. Key contributions include the development of a KV-Block-Major sparse attention kernel, integration of paged attention for MSA, optimization of the decode index scoring kernel, and the implementation of a Rust-based multimodal preprocessing gateway (SMG). These efforts resulted in 81-125% throughput improvements and addressed challenges related to sparse attention computation, KV cache management, and multimodal processing.