Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
How to install serving-llms-vllm?
moltis-org/moltis/serving-llms-vllm
$npx -y skills add moltis-org/moltis --skill serving-llms-vllmInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/moltis-org/moltis" --skill "moltis-org/moltis/serving-llms-vllm"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/moltis-org/moltis" that are relevant to the current task. Run `npx skills add "https://github.com/moltis-org/moltis"` and select the relevant skills, then follow their instructions.
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