vLLM is an open-source library for high-throughput Large Language Model (LLM) inference and serving, designed for production environments requiring low latency. It utilizes PagedAttention to manage KV cache memory, effectively eliminating fragmentation and increasing request capacity.

• Implements PagedAttention to manage attention keys and values in non-contiguous memory, maximizing GPU memory utilization.
• Supports continuous batching to process incoming requests dynamically, reducing wait times and increasing throughput.
• Provides an OpenAI-compatible API server for integration with existing GPT-based workflows.
• Enables distributed inference via Ray for scaling models across multi-GPU and multi-node configurations.
• Optimizes performance for Mixture-of-Experts (MoE) architectures, including DeepSeek-V3 and Mixtral.

Technical Context:

• Built on PyTorch with optimized kernels for NVIDIA CUDA (Blackwell/Hopper), AMD ROCm, and Google TPU.
• Requires Linux and Python 3.9+ for deployment.

Use Cases:

• Self-hosting production-grade API endpoints for Llama, Qwen, and DeepSeek models.
• Powering high-concurrency real-time chat applications and large-scale batch processing.

Install vLLM via pip to start serving models with a single command.