Benchmarking LLM Inference Back Ends: VLLM, LMDeploy, MLC-LLM, TensorRT-LLM, TGI
Selecting the right inference backend for large language models is crucial for user experience and cost efficiency. A benchmark study by BentoML compared various backends, highlighting LMDeploy's decoding performance, vLLM's low TTFT, and considerations beyond performance. BentoML and BentoCloud are recommended tools for efficient AI model deployment.
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The article discusses the importance of selecting the right inference backend for large language models (LLMs) to ensure optimal user experience and cost efficiency. The BentoML engineering team conducted a benchmark study on various inference backends, including LMDeploy, MLC-LLM, vLLM, TensorRT-LLM, and Hugging Face TGI, evaluating metrics like Time to First Token (TTFT) and Token Generation Rate. Key findings revealed that LMDeploy excelled in decoding performance and TTFT, while vLLM showed low TTFT but lower token generation rates. The article also highlights considerations beyond performance when choosing an inference backend, such as quantization support, model variety, and developer experience. BentoML and BentoCloud are introduced as tools for deploying AI models efficiently. The article concludes with recommendations for selecting the most suitable backend for Llama 3 models based on benchmark results and usability studies.
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