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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Fast, scalable LLM inference and serving for any workload
High‑throughput LLM serving with PagedAttention, quantization, and multi‑hardware support, offering an OpenAI‑compatible API and seamless Hugging Face integration.
vLLM provides developers and enterprises with a high‑performance library for LLM inference and serving. It delivers state‑of‑the‑art throughput through continuous batching, PagedAttention memory management, and CUDA/HIP graph execution, while supporting a wide range of quantization methods and decoding algorithms.
The library integrates directly with Hugging Face models and offers an OpenAI‑compatible API server, streaming outputs, and multi‑LoRA support. It runs on NVIDIA, AMD, Intel, PowerPC, TPU, and specialized accelerators such as Gaudi, Spyre, and Ascend, with tensor, pipeline, data, and expert parallelism for distributed inference. Install via pip or source and scale from a single GPU to large clusters.
vLLM is ideal for teams building real‑time chatbots, batch embedding pipelines, multi‑modal applications, or serving mixture‑of‑expert models that demand both speed and cost efficiency.
When teams consider vLLM, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
High‑concurrency chatbot
Serve thousands of simultaneous chat sessions with low latency using continuous batching and streaming outputs.
Batch embedding generation
Produce dense vectors for large document collections efficiently via PagedAttention and quantized models.
Multi‑modal image‑text generation
Run LLaVA‑style models on GPU clusters, delivering combined visual and textual responses through the API.
Mixture‑of‑expert specialization
Deploy Mixtral or Deepseek‑V2 models with expert parallelism for domain‑specific tasks at scale.
vLLM runs on NVIDIA, AMD, Intel CPUs/GPUs, PowerPC CPUs, TPU, and accelerator plugins like Gaudi, Spyre, and Ascend.
Install via pip (`pip install vllm`) or build from source following the repository instructions.
Yes, it includes an API server that mimics OpenAI endpoints and supports streaming responses.
Supported quantizations include GPTQ, AWQ, AutoRound, INT4, INT8, and FP8.
Use tensor, pipeline, data, or expert parallelism provided by vLLM to distribute workloads across GPU clusters.
Project at a glance
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