{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "zagitta-gpu-unlock--faster-bf16-lora-fine-tuning-on-a-geforce-card",
 "repo": "Zagitta/gpu-unlock",
 "repo_url": "https://github.com/Zagitta/gpu-unlock",
 "repo_slug": "zagitta-gpu-unlock",
 "title": "Run BF16 LoRA fine-tuning faster on a GeForce card",
 "page_title": "How to run BF16 LoRA fine-tuning faster on a GeForce card with GPU Unlock",
 "summary": "Unlock full tensor rate before a long LoRA or full fine-tune so training hours drop.",
 "who": "A hobbyist or researcher fine-tuning models in BF16 on a single consumer GPU.",
 "problem": "Training runs on GeForce take far longer than the hardware could manage because of the tensor cap.",
 "outcome": "The README reports 25 to 50 percent faster LoRA and full fine-tuning in BF16 on supported cards.",
 "steps": [
  "Check your card is in the Supported GPUs table",
  "Install the module and add `gpu_unlock.targets=all` to the kernel command line",
  "Reboot and confirm the unlock is active before starting training",
  "Time a short training run before and after and post your numbers in the repo's compatibility reports if you like"
 ],
 "agent_prompt": "Help me use Zagitta/gpu-unlock before a BF16 LoRA run on my <card>: confirm my card is supported, install it, and write a short timing script to compare steps per second before and after.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "This card is written from the Zagitta/gpu-unlock README, not from a test run by Deduped. Before you start, make sure you have Linux. Check the repo for newer instructions before you rely on these steps.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "c",
 "requirements": [
  "Linux (Windows is not supported; the README points to a third party port)",
  "An Ampere RTX 30 card (stable) or an Ada RTX 40 or Turing RTX 20 card (experimental); RTX 50 is not supported",
  "Root access to install a kernel module and edit the kernel command line"
 ],
 "risk": "sensitive",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "system-change",
   "notice": "Kernel level change; laptops only stay unlocked if the dedicated GPU is never fully powered off."
  }
 ],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/zagitta-gpu-unlock/faster-bf16-lora-fine-tuning-on-a-geforce-card/",
 "md": "https://dedupedai.com/use-cases/zagitta-gpu-unlock/faster-bf16-lora-fine-tuning-on-a-geforce-card/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/zagitta-gpu-unlock--faster-bf16-lora-fine-tuning-on-a-geforce-card.json",
 "api_version": "v1"
}