Prefill and Decode for Concurrent Requests - Optimizing LLM Performance

this is classic optimization, but it's critically important for anything handling high throughput. prefill and decode isn't just an academic concept; it’s how we squeeze more work out of expensive GPU cycles when handling concurrent requests. the bottleneck isn't just generating the tokens; it's the sequential overhead of preparing the context and then generating the response.

by separating the prefill phase—where the context is processed—from the decode phase—where the output stream happens—we drastically reduce the idle time and improve GPU utilization. we're talking about reducing latency for concurrent users by effectively overlapping computation. if your service handles more than a handful of simultaneous requests, ignoring this optimization is just asking for a slow, expensive system.