vLLM
vLLM is an open-source LLM inference and serving engine the operator runs on its own hardware to serve models behind an OpenAI-compatible API.
Description
vLLM is an open-source library for LLM inference and serving that the operator deploys on its own GPUs or CPUs. It manages attention key and value memory with PagedAttention and batches incoming requests continuously to raise throughput. It exposes an OpenAI-compatible API server so existing clients point at a self-hosted endpoint. The project supports a range of hardware including NVIDIA, AMD, and CPU targets, and is released under Apache 2.0.
Solution
vLLM is the inference backend, not the agent loop itself. An agent or application sends generation requests to a vLLM server through its OpenAI-compatible API; vLLM batches incoming requests continuously, manages attention key and value memory with PagedAttention, and returns generations to the caller, who runs the surrounding agent orchestration.
Primary use cases
- self-hosted LLM inference and serving
- high-throughput batched model serving
- OpenAI-compatible endpoints on operator-owned hardware
- running open-weight models across diverse accelerators
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