serving-llms-vllm

You can serve LLMs with an OpenAI-compatible API by deploying vLLM to expose an OpenAI-style /v1 server interface, allowing you to reuse existing OpenAI SDK or client integrations for high-throughput inference.

PagedAttention and continuous batching improve LLM serving throughput by mixing prefill and decode requests, which optimizes GPU memory utilization and reduces latency for multi-user chatbot and batch generation workloads.

Yes, you can run large models across multiple GPUs using tensor parallelism in vLLM, which allows you to distribute model execution to handle models that exceed single-GPU memory limits.

Yes, vLLM supports optional quantization formats including AWQ, GPTQ, and FP8, enabling memory- and cost-efficient deployment of large language models with limited GPU memory.

To start an OpenAI-compatible vLLM server, run the command `vllm serve` with your model name, specifying port and GPU memory utilization, such as `vllm serve meta-llama/Llama-3-8B-Instruct --port 8000 --gpu-memory-utilization 0.9`.

The best way to reduce time-to-first-token for production LLM workloads is to use vLLM's PagedAttention and continuous batching, which optimize prefill and decode execution for high tokens-per-second performance.