• If you're serving long-context or multimodal LLMs at scale, this breaks down concrete serving optimizations — KV-block-major sparse attention, paged MSA decode, and a Rust multimodal gateway — that cut cost and latency without sacrificing 1M-token context.
• Useful as a reference architecture for anyone running large models in production.
• How Together served MiniMax-M3 efficiently with KV-block-major sparse attention, paged MSA decode, optimized index scoring, and a Rust-based multimodal gateway.

• SDarticleServing DeepSeek-V4: why million-token context is an inference systems problemwww.together.ai
• RDarticleRecent Developments in LLM Architectures: KV Sharing, mHC, and Compressed AttentionSebastian Raschka, PhD
• CParticleCache-aware prefill–decode disaggregation (CPD) for up to 40% faster long-context LLM servingwww.together.ai

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