vLLM is a high-performance inference engine specifically designed for large language models (LLMs). It's optimized for NVIDIA CUDA-enabled GPUs and excels in multi-GPU setups. Typical use cases include production AI services that require high throughput and low latency, cloud deployments where GPU resources are available, multi-GPU inference for large models that require tensor parallelism, and scalable AI applications serving many concurrent requests.
Key benefits of vLLM are superior performance compared to general-purpose frameworks through custom CUDA kernels optimized for LLM inference operations and tensor parallelism support for efficient multi-GPU utilization. It employs techniques such as PagedAttention for reduced memory usage and increased throughput, KV cache optimization including quantization and efficient management, and continuous batching to maximize GPU utilization. vLLM offers extensive model support for popular open-source LLMs and is production-ready with wide adoption among cloud providers and AI companies. We recommend vLLM as the go-to solution for CUDA-only multi-GPU setups; it's actively developed, well-maintained, and provides significant performance improvements over alternatives, with many cloud providers using it in production because of its efficiency and scalability.