{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "htobty-readyllm--auto-tune-local-llm-speed",
 "repo": "htobty/ReadyLLM",
 "repo_url": "https://github.com/htobty/ReadyLLM",
 "repo_slug": "htobty-readyllm",
 "title": "Auto-tune a local LLM for faster generation",
 "page_title": "How to auto-tune a local LLM for faster generation with ReadyLLM",
 "summary": "Run two-phase auto tuning on your own machine to find parameters that fit your hardware.",
 "who": "A developer whose local model runs slowly on the CPU and whose GPU sits mostly idle.",
 "problem": "Default engine settings often leave much of the model on the CPU, so throughput stays low.",
 "outcome": "Benchmarked parameter sets for your goal (latency, throughput, or prefill), saved as default deployment parameters.",
 "steps": [
  "Run `cd backend` then `pip install -r requirements.txt`, then `uvicorn app.main:app --host 127.0.0.1 --port 8000`",
  "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",
  "On the Tuning page, pick a goal (latency, throughput, or prefill) and run auto tuning",
  "Review the benchmark results and use the tuned parameters as defaults on the Deploy page"
 ],
 "agent_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.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "60 minutes",
 "basis": "readme",
 "caveat": "The README states the 50% speedup as a claim for one tested setup, so your gain depends on your own hardware and model.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/htobty-readyllm/auto-tune-local-llm-speed/",
 "md": "https://dedupedai.com/use-cases/htobty-readyllm/auto-tune-local-llm-speed/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/htobty-readyllm--auto-tune-local-llm-speed.json",
 "api_version": "v1"
}