Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
vLLM
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.
Overview
Overview
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.
Flexibility & Deployment
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.
Who Benefits
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.
Highlights
PagedAttention enables efficient memory use for long contexts
Continuous batching delivers state‑of‑the‑art serving throughput
Broad hardware support with GPU, CPU, TPU, and accelerator plugins
OpenAI‑compatible API server with streaming and multi‑LoRA
Pros
- High inference throughput for large request volumes
- Flexible deployment across many hardware platforms
- Extensive quantization options reduce memory and cost
- Seamless integration with Hugging Face and OpenAI API
Considerations
- Best performance requires GPU or accelerator hardware
- Distributed setup can add operational complexity
- CPU‑only inference may have limited speed
- Advanced features have a learning curve for new users
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Fit guide
Great for
- Enterprises needing scalable, high‑throughput LLM APIs
- Developers building real‑time conversational services
- Researchers experimenting with quantized or MoE models
- Teams deploying multi‑modal models such as LLaVA
Not ideal when
- Edge devices lacking GPU or accelerator support
- Small hobby projects where setup overhead outweighs benefits
- Latency‑critical single‑request use cases preferring low‑latency over throughput
- Environments without CUDA/HIP or compatible drivers
How teams use it
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.
Tech snapshot
Python86%
Cuda8%
C++5%
Shell1%
C1%
CMake1%
Frequently asked questions
Which hardware platforms does vLLM support?
vLLM runs on NVIDIA, AMD, Intel CPUs/GPUs, PowerPC CPUs, TPU, and accelerator plugins like Gaudi, Spyre, and Ascend.
How do I install vLLM?
Install via pip (`pip install vllm`) or build from source following the repository instructions.
Can vLLM serve models through an OpenAI‑compatible API?
Yes, it includes an API server that mimics OpenAI endpoints and supports streaming responses.
What quantization methods are available?
Supported quantizations include GPTQ, AWQ, AutoRound, INT4, INT8, and FP8.
How can I scale inference across multiple GPUs?
Use tensor, pipeline, data, or expert parallelism provided by vLLM to distribute workloads across GPU clusters.
Project at a glance
Active- Stars
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LicenseApache-2.0
Repo age2 years old
Last commit2 days ago
Primary languagePython
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