vLLM
Open SourceFundedHigh-throughput, memory-efficient LLM inference and serving engine
About vLLM
vLLM is an open-source inference and serving engine designed to optimize the deployment of large language models (LLMs) across diverse hardware platforms. It targets enterprises seeking to maximize throughput and minimize inference costs by leveraging advanced scheduling, continuous batching, and PagedAttention techniques. The platform supports a wide range of open-source LLMs and offers a drop-in OpenAI-compatible API for seamless integration into existing workflows.
Built for organizations requiring scalable and cost-efficient LLM infrastructure, vLLM enables deployment on NVIDIA CUDA GPUs, AMD ROCm GPUs, Huawei Ascend NPUs, AWS Neuron chips, Google TPUs, IBM Spyre AI accelerators, Intel Gaudi, and Apple Silicon Metal, among others. Its universal compatibility and hardware-agnostic design allow enterprises to optimize GPU utilization and reduce operational expenses while maintaining high performance. The active community and comprehensive documentation further support enterprise adoption and troubleshooting.
Key Capabilities
- ✓High-throughput LLM inference with PagedAttention
- ✓Advanced scheduling and continuous batching for GPU efficiency
- ✓Drop-in OpenAI-compatible API for easy integration
- ✓Universal hardware compatibility across GPUs and accelerators
- ✓Support for a wide range of open-source LLM models
Integrations
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