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How to use ReadyLLM: what you can do with it

What is ReadyLLM? ReadyLLM is a Python and React web app that deploys, monitors, and tunes local or LAN large language models, and runs ComfyUI image and video generation.

How do I use it? 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. Full steps below.

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What do you want to do?

4 use cases

  • Auto-tune a local LLM for faster generation

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

    View prompt
    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.

  • Deploy and watch an inference server on a LAN GPU box

    Add a remote machine over SSH, start an engine there, and watch its GPU and VRAM live.

    View prompt
    Using the repo htobty/ReadyLLM, start the backend and frontend, add the remote SSH target <target-name> at <host-address>, and deploy a downloaded model with the engine the target has. Report the service status and the current GPU and VRAM readings from Monitor, and verify the service responds before finishing.

  • Generate images and short videos through ComfyUI

    Use the control panel to run text-to-image, text-to-video, and storyboard jobs on a ComfyUI target.

    View prompt
    Using the repo htobty/ReadyLLM, start the backend and frontend, connect the ComfyUI target <target-name>, and generate one text-to-image result from the prompt <prompt-text>. Report where the output file is previewed and confirm the preview loads in the control panel.

  • Let your own LLM pick and refine engine parameters

    Connect an LLM API to AI Agent tuning so it reasons out parameters and improves them over several rounds.

    View prompt
    Using the repo htobty/ReadyLLM, start the backend and frontend, configure the AI tuning API with the URL <api-url> and model name <model-name>, and run AI Agent tuning on target <target-name>. Report the best round's parameters and its benchmark result, and confirm the API key is masked when the settings are read back.

Quick start with ReadyLLM

  1. Check you have what it needs: 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.
  2. Open htobty/ReadyLLM on GitHub and read the README and license.
  3. Run cd backend then pip install -r requirements.txt, then uvicorn app.main:app --host 127.0.0.1 --port 8000
  4. In a second terminal, run cd frontend then npm install && npm run dev
  5. Open http://localhost:3000, go to Settings, add the local machine, and click Test connection to view hardware info
  6. On the Tuning page, pick a goal (latency, throughput, or prefill) and run auto tuning

These steps come from our top use case, Auto-tune a local LLM for faster generation, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forDevelopers and AI operators who run local or LAN inference servers and want help choosing parameters, deploying engines, and watching GPU performance.
  • Why it is hot100 stars, launched 56 days ago
  • LanguagePython
  • Sourcegithub.com/htobty/ReadyLLM · Back to Hot repos

Requirements

  • 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

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Questions about ReadyLLM

What is ReadyLLM?

ReadyLLM is a Python and React web app that deploys, monitors, and tunes local or LAN large language models, and runs ComfyUI image and video generation. It is an open-source project on GitHub: https://github.com/htobty/ReadyLLM.

How do I use ReadyLLM?

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. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is ReadyLLM for?

Developers and AI operators who run local or LAN inference servers and want help choosing parameters, deploying engines, and watching GPU performance.

What can I do with ReadyLLM?

4 use cases so far, for example: Auto-tune a local LLM for faster generation; Deploy and watch an inference server on a LAN GPU box; Generate images and short videos through ComfyUI. Each has steps and a copy-paste agent prompt.

What do I need to run ReadyLLM?

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.

What are alternatives to ReadyLLM?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is ReadyLLM safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.