vLLM

vLLM is an open-source, production-grade large language model inference engine developed by UC Berkeley. Its core innovation, the PagedAttention technique, boosts GPU memory utilization by 24 times. It supports both continuous and dynamic batching, delivering significantly higher throughput in high-concurrency scenarios compared to similar solutions. The engine also enables distributed multi-GPU inference, AWQ/GPTQ/FP8 quantization, and is compatible with the OpenAI API format. It has been integrated by major cloud providers such as AWS, Google Cloud, and Alibaba Cloud. If your application requires an LLM API service with high concurrency and low latency, vLLM represents the best choice for production environments today.