Benchmarking LLM Inference Backends

Choosing the right inference backend for serving large language models is critical for performance and cost efficiency. The BentoML engineering team benchmarked several backends—vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and Hugging Face TGI—using Llama 3 models on an A100 80GB GPU across varying user loads. Key findings include the strong correlation between token generation rate and GPU utilization, alongside considerations like ease of use, model compilation requirements, and stable releases. Recommendations for each backend's suitability under different scenarios are provided, with BentoML facilitating consistent integration and minimal performance overhead.