Prefill and Decode for Concurrent Requests - Optimizing LLM Performance

Handling concurrent requests is essential for optimizing the performance of Large Language Model (LLM) applications, which is especially crucial when dealing with metrics such as latency, throughput, and GPU resource utilization. The process of text generation in LLMs involves two distinct phases: prefill, where input tokens are calculated in parallel, and decode, which processes each output token sequentially. Prefill is GPU-intensive and benefits from parallelization, while decode is typically limited by GPU memory bandwidth. Static batching processes requests simultaneously but can lead to inefficiencies and delays, particularly in time to first token. Continuous batching strategies, like prefill-first and chunked prefill, offer improvements by allowing new requests to be processed as they arrive, thereby reducing waiting times and improving resource utilization. Prefill-first strategies can minimize initial delay but may interrupt the decode phase, while chunked prefill balances computational demands more efficiently, increasing total token throughput by allowing decoding to occur alongside prefill operations. This method, now universally implemented for self-hosted LLMs at TNG, enhances overall efficiency despite the complexities of optimizing chunk sizes in unpredictable environments.