Modal details how to serve trillion-parameter coding agents at trillion-token scale
What happened
Modal 工程团队写了一篇长文，拆解他们怎么为 Moonshot AI 的 Kimi K2.6 模型做推理加速，用来驱动编码 Agent。核心成果是把单副本（replica）性能提了 2.8 倍（按单用户交互体验算）和 5.6 倍（按多用户并发吞吐算），让原本贵到没法用的服务变得价格有竞争力。文章先讲工作负载：万亿参数的混合注意力 MoE 模型，输入...
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- AI HOT (Curated Pool)PickModal details how to serve trillion-parameter coding agents at trillion-token scale
Modal's engineering team published a deep-dive on serving Moonshot AI's Kimi K2.6 for coding agents. They boosted per-replica performance by 2.8x per user and 5.6x across users, turning a ruinously expensive service into a price-competitive one. One service processed hundreds of billions of tokens per day and trillions in aggregate. The post walks through workload analysis for hybrid-attention MoE models and the engineering optimizations applied. Exact GPU models and per-request latency numbers are not disclosed in the body.