vLLM significantly speeds up LLM serving by efficiently managing attention KV memory with PagedAttention and employing continuous batching. It supports a wide range of quantization methods and optimized CUDA kernels for faster model execution.
A high-throughput and memory-efficient inference engine for LLMs.
Developers and researchers needing to deploy and serve Large Language Models with high performance and reduced resource usage.