VLLM: High-Throughput LLM Inference and Serving

Introduce and detail vLLM, a prominent open-source library designed for high-throughput and memory-efficient Large Language Model (LLM) inference. They explain its core innovations like PagedAttention and continuous batching, highlighting how these techniques revolutionize memory management and significantly boost performance compared to traditional systems.

The text also outlines vLLM's architecture, including the recent V1 upgrades, its extensive features and capabilities (covering performance, memory, flexibility, and scalability), and its strong integration with MLOps workflows and various real-world applications across NLP, computer vision, and RL.

Finally, the sources discuss comparisons with other serving frameworks, vLLM's robust development community and governance structure (including its move to the PyTorch Foundation), installation requirements, and an ambitious future roadmap aimed at enhancing scalability, production readiness, and support for emerging AI models and hardware.