vLLM Free

What is vLLM?

vLLM is a fast and easy-to-use library for LLM inference and serving, originally developed at UC Berkeley. Its core innovation, PagedAttention, manages attention key-value memory in non-contiguous blocks, cutting memory waste and enabling far higher batch throughput than naive implementations. vLLM ships an OpenAI-compatible server, supports tensor parallelism, quantization, and a wide range of model architectures.

Product Features

• PagedAttention for efficient KV-cache memory management
• OpenAI-compatible HTTP server out of the box
• Continuous batching for maximum GPU utilization
• Tensor and pipeline parallelism for multi-GPU deployment
• Quantization support (GPTQ, AWQ, FP8)
• Broad model support: Llama, Mistral, Qwen, Mixtral, and more

Product Highlights

• Industry-leading throughput and latency for LLM serving
• Drop-in OpenAI API compatibility eases migration
• Scales from a single GPU to multi-node clusters

Use Cases

• Serving a chatbot backend to thousands of concurrent users
• Hosting self-managed open models for enterprise privacy
• Running large batch inference jobs for data processing
• Powering RAG pipelines with a fast local model server
• Benchmarking and comparing open-weight model performance