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How to auto-tune a local LLM for faster generation with ReadyLLM

Run two-phase auto tuning on your own machine to find parameters that fit your hardware.

Short answer: Run cd backend then pip install -r requirements.txt, then uvicorn app.main:app --host 127.0.0.1 --port 8000; then in a second terminal, run cd frontend then npm install && npm run dev; open http://localhost:3000, go to Settings, add the local machine, and click Test connection to view hardware info. All steps.

Who it is forA developer whose local model runs slowly on the CPU and whose GPU sits mostly idle.

The problemDefault engine settings often leave much of the model on the CPU, so throughput stays low.

The outcomeBenchmarked parameter sets for your goal (latency, throughput, or prefill), saved as default deployment parameters.

What you need first

  • Python 3.9+ and Node.js 18+ on the control machine
  • A target machine with llama.cpp, vLLM, or ComfyUI already installed, or installable through the tool
  • Network or SSH access to any remote target you want to manage

Steps

  1. Run cd backend then pip install -r requirements.txt, then uvicorn app.main:app --host 127.0.0.1 --port 8000
  2. In a second terminal, run cd frontend then npm install && npm run dev
  3. Open http://localhost:3000, go to Settings, add the local machine, and click Test connection to view hardware info
  4. On the Tuning page, pick a goal (latency, throughput, or prefill) and run auto tuning
  5. Review the benchmark results and use the tuned parameters as defaults on the Deploy page

Prompt for your agent

Using the repo htobty/ReadyLLM, start the backend on 127.0.0.1:8000 and the frontend on localhost:3000, register the local machine as target <target-name>, and run auto tuning for the throughput goal. Report the benchmark results and confirm the tuned parameters are available as default deployment parameters.

Caveat. The README states the 50% speedup as a claim for one tested setup, so your gain depends on your own hardware and model.

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