A Simple Guide to LLM Serving: Quantization, KV Caching, and Continuous Batching
When you type a prompt into ChatGPT or Claude, the model generates a response word-by-word (or token-by-token) in real-time. Behind this smooth user interface lies a massive engineering challenge: LLM Inference is incredibly expensive and slow. If you run a base LLM without optimizations, it will devour your GPU memory, process requests one-by-one, and make your users wait. To solve this, developers and researchers use three core serving techniques that work in tandem to speed up generation by up to 10x while drastically cutting hardware costs. ...