In our previous benchmarking blog post, we compared the performance of different inference backends using two key metrics: Time to First Token and Tok

Best Practices for Tuning TensorRT-LLM for Optimal Serving with BentoML

submited by

Style Pass

2024-09-20 03:00:05

In our previous benchmarking blog post, we compared the performance of different inference backends using two key metrics: Time to First Token and Token Generation Rate. We intentionally did not tune the inference configurations, such as GPU memory utilization, maximum number of sequences, and paged KV cache block size, to implicitly measure the performance and ease-of-use of each backend, highlighting their practicality in real-world applications.

In this blog post, the BentoML engineering team shifts focus to the impact of performance tuning, specifically examining how tuning inference configurations can significantly enhance the serving performance of large language models (LLMs) using TensorRT-LLM (TRT-LLM). By adjusting key parameters like batch size and prefix chunking, we aim to demonstrate the substantial improvements that can be achieved.

This post serves as a comprehensive guide for optimizing TRT-LLM settings, offering practical insights and detailed steps to help you achieve superior performance. Specifically, it will cover