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How to serve several LoRA fine-tunes from one base model with vLLM

Run multiple adapters on a single loaded model instead of loading a full copy for each.

Who it is forAn ML engineer who maintains several fine-tuned variants of the same base model for different customers or tasks.

The problemLoading a full model per fine-tune wastes GPU memory and makes each variant expensive to host.

The outcomeOne server that answers requests for each adapter by name, sharing the base model weights.

What you need first

  • Python with uv or pip installed
  • A supported NVIDIA, AMD or Intel GPU, or an x86, ARM or PowerPC CPU
  • A Hugging Face model you are allowed to download and use

Steps

  1. Install vLLM with uv pip install vllm.
  2. Start the server for your base model with LoRA support enabled, following the Quickstart and supported models docs. (inferred, check the README)
  3. Register each LoRA adapter path with the server as described in the official documentation. (inferred, check the README)
  4. Send one request per adapter and compare the replies to the base model's reply.
  5. Record which adapter names the server accepts.

Prompt for your agent

Using the vllm-project/vllm repo, serve the base model <base-model-id> with the LoRA adapters at <adapter-paths>. Send the same prompt to each adapter and the base model, then report the differences. Verify that each adapter name returns a reply.

Caveat. The README confirms multi-LoRA support but not the flags or adapter registration steps, so verify those in the official docs.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for vllm-project/vllm and "Serve several LoRA fine-tunes from one base model"

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