Article "Prefill and Decode for Concurrent Requests - Optimizing LLM Performance"

At TNG, we are self-hosting numerous Large Language Models on our cluster of 24 H100 GPUs. It supports 50 different applications, handles over 5,000 inferences per hour, and generates more than ten million tokens every day. Effective prompt processing is a crucial aspect of ensuring a great user experience through low-latency responses and optimal performance in high-traffic, multi-user environments.

In the second part of our series on LLM performance, Benjamin Merkel discusses concurrent processing of requests and shares valuable insights on optimization strategies such as continuous batching and chunked prefill.

Read the full article “Prefill and Decode Strategies for Concurrent Requests – Optimizing LLM Performance” on Hugging Face here.