vLLM
vllm-project/vllm
High-throughput LLM serving engine for production inference (PagedAttention). GitHub: vllm-project/vllm (~89k stars).
GitHub repository
Overview
vLLM is the open-source project at vllm-project/vllm on GitHub (https://github.com/vllm-project/vllm). High-throughput LLM serving engine for production inference (PagedAttention). Category on LimeDock: ai-powered. Approximate community size: ~89k GitHub stars. Repo (copy/paste): https://github.com/vllm-project/vllm This LimeDock Directories page explains what it is and how teams use it in plain English — so searches for the GitHub project can land on a practical guide.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/vllm-project/vllm GitHub path: vllm-project/vllm
How to open it: 1. Copy this URL: https://github.com/vllm-project/vllm 2. Clone or follow the README install for your OS / stack. 3. Start with the smallest example in their docs before production use.
How to use it
**Simple example** Prototypes work in notebooks; you now need faster multi-user model serving.
1. Deploy vLLM with a supported model. 2. Hit the OpenAI-compatible API from your app. 3. Load-test before marketing the feature.
Example prompts
- “Deploy vLLM for a 7B model on our GPU box.”
- “vLLM vs Ollama for a multi-user internal app?”
- “Tuning tips for latency vs throughput.”
Use cases and examples
- Evaluate vllm-project/vllm for your stack
- Onboard a teammate to vLLM with a shared checklist
- Compare vLLM against tools you already pay for
- Capture lessons in your internal wiki after a pilot
Prerequisites
- GPU server
- Model license
- Ops ownership
Tips
- Serving ≠ training.
- Put auth in front of any public endpoint.