Prefill and Decode for Concurrent Requests: Optimizing LLM Throughput with vLLM on Llama-3.1-8B (H100)

AI Impact Summary

This piece describes how LLM inference can be optimized under concurrent load via a two-stage generation process: prefill (parallelizable across input tokens) and decode (mostly sequential). It highlights batching strategies (static vs continuous) and how they impact latency (time to first token) and throughput (tokens per second) on GPU hardware, using vLLM with Llama-3.1-8B on an H100 cluster as an example. For operators, the takeaway is to tune concurrency, batching, and KV-cache reuse to meet interactive latency targets (3s TTF, 100–300ms per token) while maximizing GPU utilization; this requires instrumentation to measure per-token latency and adapt batch sizes in real time. The business consequence is that improper batching can waste GPU resources and push response times beyond acceptable levels, reducing user satisfaction and throughput across 50 apps and 5k inferences/hour.