The vLLM engine is currently one of the top-performing ways to execute large language models (LLM). It provides the vllm serve command as an easy option to deploy a model on a single machine. While this is convenient, to serve these LLMs in production and at scale some advanced features are necessary.

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The vLLM engine is optimized for executing large language models and can be easily deployed using the vllm serve command. For production environments, it's beneficial to pair vLLM with TorchServe, which offers essential features like custom metrics and model versioning. The post outlines steps to deploy the Llama-3.1-70B model using a custom Docker image, showcasing configurations for efficient GPU utilization and asynchronous request handling. Key functionalities of vLLM, such as PagedAttention and continuous batching, are highlighted along with the integration process with TorchServe.

Deploying LLMs with TorchServe + vLLM

Quickly getting started with Llama 3.1 on TorchServe + vLLM