LLM inference: how it works

LLM inference generates responses through frozen model weights in a prefill phase that processes an entire prompt and determines time to first token, followed by a memory-bandwidth-limited decode loop that produces one token at a time and determines streaming speed. Effective serving requires balancing latency, throughput, and cost, measured through time to first token, time per output token, end-to-end latency, throughput, and tail-percentile performance under realistic concurrency. Batching improves GPU utilization by sharing weight reads across requests, with continuous batching, chunked prefill, and prefill-decode disaggregation helping manage the conflict between prompt processing and active response streams. Major optimization methods include speculative decoding, quantization, attention and KV-cache improvements, parallelism, and selecting smaller models when quality requirements permit, while each technique differs in phase impact and potential effect on outputs. Costs vary between per-token serverless APIs and dedicated GPU deployments, with the preferable model depending on traffic volume, utilization, latency targets, model size, and the mix of input and output tokens.

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