vLLM High-Throughput LLM Serving Engine with PagedAttention
vLLM is a fast and memory-efficient inference and serving engine for large language models. It uses PagedAttention for efficient memory management, supports continuous batching, and provides an OpenAI-compatible API server for production-grade LLM deployment.
vLLM is a fast and memory-efficient inference and serving engine for large language models. It uses PagedAttention for efficient memory management, supports continuous batching, and provides an OpenAI-compatible API server for production-grade LLM deployment.
npx skills add agentskillexchange/skills --skill vllm-high-throughput-llm-serving
vLLM is a high-throughput, memory-efficient inference and serving library for large language models. Developed at UC Berkeley’s Sky Computing Lab, it has become the standard for production LLM serving, used by major companies and cloud providers for deploying models at scale.
What It Does
vLLM serves LLMs with state-of-the-art throughput using its innovative PagedAttention algorithm, which manages attention key-value memory like virtual memory pages in operating systems. This eliminates memory waste from fragmentation and enables efficient batching of concurrent requests. The engine supports NVIDIA GPUs (CUDA), AMD GPUs (HIP/ROCm), Intel GPUs (SYCL), Google TPUs, and CPU inference.
How Agents Use It
Agents deploy vLLM as a production inference backend via its OpenAI-compatible API server. The vllm serve command launches an HTTP server that accepts standard chat completion and text completion requests. Agents can leverage features like structured output with JSON schema constraints, function calling, multi-LoRA serving for personalized models, and prefix caching for repeated context. vLLM handles batching, scheduling, and memory management automatically.
Key Features
- PagedAttention for efficient attention key-value memory management
- Continuous batching of incoming requests for maximum throughput
- GPTQ, AWQ, INT4, INT8, and FP8 quantization support
- Tensor, pipeline, data, and expert parallelism for distributed inference
- Speculative decoding and chunked prefill
- OpenAI-compatible API server with streaming support
- Multi-LoRA serving for concurrent adapter models
- Prefix caching for repeated prompts
- Support for 100+ Hugging Face model architectures
Integration Points
Install with pip install vllm. Launch a server with vllm serve meta-llama/Llama-3-8B-Instruct. The API is compatible with OpenAI client libraries, so any agent using the OpenAI SDK can switch to vLLM by changing the base URL. Supports models from Hugging Face including LLaMA, Mistral, Mixtral, DeepSeek, Qwen, Gemma, and multimodal models like LLaVA.