vLLM is a high-throughput, memory-efficient inference and serving engine for LLMs. Optimize your AI deployment.
About
vLLM is a high-performance, open-source inference and serving engine specifically designed for large language models (LLMs). Through innovative attention algorithms and advanced memory management techniques, it significantly boosts throughput for generative AI models while reducing computational costs, making it easier and more economical to deploy and run large language models at scale.
Key Features
Pricing & Fees
- Pricing: Not publicly listed on the pages we reviewed; check the official site for current plans.
- Free and open source: vLLM is a community project distributed as free, open-source software, so there is no license fee to run it yourself.
- Installation options: Stable and nightly builds are available via pip/uv and Docker, requiring Python 3.10+ (3.12+ recommended).
- Cross-platform support: Runs on NVIDIA CUDA, AMD ROCm, Intel Gaudi/XPU, CPU, Google Cloud TPU, AWS Neuron, Apple Silicon, and other hardware through a unified API.
- OpenAI-compatible API: Includes a drop-in OpenAI-compatible API, allowing integration with existing tooling without additional gateway costs.
Who It's For
vLLM is ideal for enterprise developers, researchers, and technical teams building or deploying large language model applications. Whether you are a tech company requiring high-performance model serving, a small or mid-sized team looking to cut inference costs, or a startup prototyping AI products, vLLM enables more efficient and stable model serving for your use case.
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