Fast LLM serving with PagedAttention
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Tracked since2025
0 reviews tracked·3 press mentionsThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Fast LLM inference
Biggest con
Hardware requirements
TL;DR - vLLM
- vLLM is a high-throughput LLM serving library optimized for inference
- It achieves 24x higher throughput than HuggingFace with PagedAttention
- Completely free and open-source
Pricing: Free forever
Best for: Individuals & startups
What is vLLM?
vLLM serves LLMs with optimized throughput. Efficient inference for language models-running AI at production scale.
The throughput is excellent. The memory efficiency is smart. The production features are growing.
Teams deploying LLMs at scale use vLLM for efficient model serving.
Available on: Linux
Pros & Cons
Pros
- Fast LLM inference
- Open source
- Good performance
- Active development
- Good for production
Cons
- Hardware requirements
- Setup complexity
- Learning curve
- Documentation improving
- Still maturing
Key Features
LLM servingPagedAttentionHigh throughputOpenAI compatibleContinuous batchingOpen source
Pricing Plans
Pricing checked Jul 14, 2026
Free
Free
- High-throughput LLM serving
- PagedAttention
- OpenAI-compatible API
- GPU optimization
- Apache-2.0 license
- Open source
Reviews
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vLLM FAQ
How does vLLM achieve fast LLM inference?
vLLM achieves fast LLM inference through optimized throughput, utilizing techniques like PagedAttention. This allows it to serve large language models efficiently, especially for AI at production scale.
Which teams benefit most from using vLLM?
Teams deploying large language models at scale will find vLLM most beneficial for efficient model serving. It is designed for production environments where high performance and optimized throughput are critical.
How is vLLM priced?
vLLM is free to use, as it is an open-source project and does not require a paid plan. This makes it accessible for organizations looking to deploy LLMs without licensing costs.
Can vLLM be used for deploying AI models in a cloud environment?
Yes, vLLM is suitable for deploying AI models in a cloud environment, particularly within GPU Cloud and Hosting & Deployment contexts. It is designed for efficient inference of language models at production scale.
What are the main trade-offs when implementing vLLM?
Implementing vLLM involves trade-offs such as specific hardware requirements, potential setup complexity, and a learning curve. While its documentation is improving, the product is still maturing.
How does vLLM compare to Together AI for LLM serving?
vLLM, like Together AI, focuses on efficient LLM serving, but vLLM is an open-source solution known for its fast LLM inference and active development. It offers good performance and is designed for production use cases.
Source: vllm.ai