How an LLM request is processed
Why we picked it
For developers using LLMs, this explains how to optimize performance and reduce costs by understanding the underlying engine mechanics.
An engineer explains the lifecycle of an LLM request: each forward pass produces one token. Prefill consumes compute, decode uses GPU memory. KV cache and continuous batching work together. Prefix caching reduces the computational load for 100-agent loops from 55x to 10x.
Original · shao__meng
一个请求进入 LLM 推理引擎后，到底经历了什么？ Together AI 工程师 Zain 讲明白了一个请求的一生：一次传播产一个 token，prefill 吃算力、decode 吃显存，KV 缓存、连续批处理层层接力，Agent 循环再把 GPU 榨干。前缀缓存让 100 轮 Agent 计算量从 55 倍降到 10 倍。 x.com/i/article/2098… 💬 1 🔄 2 ❤️ 8 👀 551 📊 4 ⚡