vLLM

vLLM is an open-source library designed to deliver high-throughput, low-latency inference for large language models on GPU hardware. It focuses on efficient memory management, batching, and throughput optimizations to make serving transformer-based models faster and more resource-efficient. vLLM exposes a Python API and runtime components that let developers run and integrate models in self-hosted environments.

• High-performance inference: Optimized runtimes and batching strategies to maximize GPU utilization for transformer models.
• Memory-efficient management: Techniques for KV-cache and attention memory management to reduce GPU memory pressure.
• Python API and SDK: Programmatic interfaces for loading models, running inference, and integrating into applications.
• Support for common model formats: Designed to run models exported in widely used formats and to interoperate with popular model toolchains.

Getting started typically involves installing or building the library from source, preparing a GPU-enabled environment, loading a compatible model, and invoking the Python API to perform inference. The documentation provides guides on configuration, performance tuning, and deployment patterns for self-hosted inference services.