# AI use cases for ReadyLLM (htobty/ReadyLLM) Repo: [htobty/ReadyLLM](https://github.com/htobty/ReadyLLM) | Use cases: 4 | Page: https://dedupedai.com/use-cases/htobty-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. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Auto-tune a local LLM for faster generation: https://dedupedai.com/use-cases/htobty-readyllm/auto-tune-local-llm-speed/ - Deploy and watch an inference server on a LAN GPU box: https://dedupedai.com/use-cases/htobty-readyllm/deploy-monitor-lan-gpu-inference/ - Generate images and short videos through ComfyUI: https://dedupedai.com/use-cases/htobty-readyllm/generate-images-videos-comfyui/ - Let your own LLM pick and refine engine parameters: https://dedupedai.com/use-cases/htobty-readyllm/ai-agent-tuning-with-own-llm/ ## How to auto-tune a local LLM for faster generation with ReadyLLM Repo: [htobty/ReadyLLM](https://github.com/htobty/ReadyLLM) | Difficulty: Intermediate | Time: 60 minutes | Basis: From the README Run two-phase auto tuning on your own machine to find parameters that fit your hardware. **Who it is for:** A developer whose local model runs slowly on the CPU and whose GPU sits mostly idle. **The problem:** Default engine settings often leave much of the model on the CPU, so throughput stays low. **The outcome:** Benchmarked parameter sets for your goal (latency, throughput, or prefill), saved as default deployment parameters. ### 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 to paste into your agent ```text 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 , 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. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/htobty-readyllm/auto-tune-local-llm-speed/ ## How to deploy and watch an inference server on a LAN GPU box with ReadyLLM Repo: [htobty/ReadyLLM](https://github.com/htobty/ReadyLLM) | Difficulty: Intermediate | Time: 90 minutes | Basis: From the README Add a remote machine over SSH, start an engine there, and watch its GPU and VRAM live. **Who it is for:** A team lead or ops engineer with a GPU server on the local network who needs to serve a model to others. **The problem:** Starting and watching an inference service on a remote GPU machine usually means juggling SSH sessions and separate monitoring tools. **The outcome:** A model served by llama.cpp, vLLM, or SGLang on the remote target, with live GPU, VRAM, throughput, and latency curves in the browser. ### 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`, and open `http://localhost:3000` 3. On Settings, add the remote machine over SSH with a key or password, confirm the OS, set the engine path and model directory, and click Test connection 4. On Model Store, download a model and confirm integrity after the download finishes 5. On Deploy, start the inference service and adjust runtime parameters, then open Monitor to watch the live curves ### Prompt to paste into your agent ```text Using the repo htobty/ReadyLLM, start the backend and frontend, add the remote SSH target at , 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. ``` ### Caveat vLLM does not run natively on Windows, so Windows targets must use WSL2 for it. Risk, credentials: Target settings are saved to ~/.model-deploy-assistant/targets.json, so limit who can read that file on a shared machine. Risk, remote script: One-click engine install can download, compile, or pip-install software on the remote machine, so check what it will run before you confirm. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/htobty-readyllm/deploy-monitor-lan-gpu-inference/ ## How to generate images and short videos through ComfyUI with ReadyLLM Repo: [htobty/ReadyLLM](https://github.com/htobty/ReadyLLM) | Difficulty: Intermediate | Time: 120 minutes | Basis: From the README Use the control panel to run text-to-image, text-to-video, and storyboard jobs on a ComfyUI target. **Who it is for:** A content creator or designer with a GPU machine running ComfyUI who wants to produce visuals without building node graphs by hand. **The problem:** ComfyUI workflows are powerful but time-consuming to set up, and results are scattered across folders. **The outcome:** Generated images or videos previewed in the control panel, with optional LLM-expanded prompts and AI upscaling. ### 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`, and open `http://localhost:3000` 3. On Settings, add the target machine that runs ComfyUI and test the connection 4. On Model Store, download an image or video model such as FLUX.1 or SDXL 5. Open the image and video generation area, enter a prompt or let an LLM expand it, run the job, and preview the result ### Prompt to paste into your agent ```text Using the repo htobty/ReadyLLM, start the backend and frontend, connect the ComfyUI target , and generate one text-to-image result from the prompt . Report where the output file is previewed and confirm the preview loads in the control panel. ``` ### Caveat ComfyUI on the target listens on its own loopback address by default, so finished-clip preview is proxied through the backend when the control side cannot reach it directly. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/htobty-readyllm/generate-images-videos-comfyui/ ## How to let your own LLM pick and refine engine parameters with ReadyLLM Repo: [htobty/ReadyLLM](https://github.com/htobty/ReadyLLM) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README Connect an LLM API to AI Agent tuning so it reasons out parameters and improves them over several rounds. **Who it is for:** An engineer who already has an LLM API and wants a tuning pass that uses reasoning instead of a fixed search. **The problem:** Manual parameter guessing is slow, and a fixed search may miss settings that suit a particular model and scenario. **The outcome:** Parameter suggestions from your LLM, refined over multiple rounds with benchmark results fed back each time. ### 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`, and open `http://localhost:3000` 3. On the Tuning page, enter your LLM API URL, key, and model name, then save them 4. Choose AI Agent tuning for the target and model, and start the iterative run 5. Review the benchmark history across rounds and apply the best parameters on Deploy ### Prompt to paste into your agent ```text Using the repo htobty/ReadyLLM, start the backend and frontend, configure the AI tuning API with the URL and model name , and run AI Agent tuning on target . Report the best round's parameters and its benchmark result, and confirm the API key is masked when the settings are read back. ``` ### Caveat AI tuning quality depends on the LLM API you supply, and the README does not name which models it works best with. Risk, credentials: Your LLM API key is sent to the API you configure, so use a key you can rotate and keep it out of shared screenshots. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/htobty-readyllm/ai-agent-tuning-with-own-llm/