{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "zagitta-gpu-unlock--speed-up-local-llm-prefill-on-an-rtx-3090",
 "repo": "Zagitta/gpu-unlock",
 "repo_url": "https://github.com/Zagitta/gpu-unlock",
 "repo_slug": "zagitta-gpu-unlock",
 "title": "Speed up local LLM prompt processing on an RTX 30 card",
 "page_title": "How to speed up local LLM prompt processing on an RTX 30 card with GPU Unlock",
 "summary": "Install the gpu_unlock module so FP16 and BF16 tensor math runs at full rate for vLLM, PyTorch and Transformers.",
 "who": "A home lab user serving unquantized or AWQ models on an RTX 3060 to 3090 who waits on long prompts.",
 "problem": "GeForce cards run tensor core math with FP32 accumulation at half the rate of the same silicon sold as a pro card.",
 "outcome": "Faster prefill and time to first token on FP16, BF16, AWQ or GPTQ models, with bit-identical outputs.",
 "steps": [
  "Run `git clone https://github.com/Zagitta/gpu-unlock`, `cd gpu-unlock` and `make install`",
  "Append `gpu_unlock.targets=all` to your kernel command line (GRUB, systemd-boot or UKI, as the README shows)",
  "On Debian or Ubuntu run `sudo update-grub`, then reboot",
  "Rerun your usual vLLM or Transformers benchmark and compare prefill speed"
 ],
 "agent_prompt": "Walk me through installing Zagitta/gpu-unlock on this <distro> machine with an <RTX card>: build the module, add gpu_unlock.targets=all to the kernel command line for my bootloader, and give me a before and after benchmark to run.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "45 minutes",
 "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": "This installs a kernel module that loads before the NVIDIA driver; keep a known good boot entry in case the system does not start."
  },
  {
   "tag": "unofficial",
   "notice": "This is not supported by NVIDIA. The README says harm is very unlikely but the card may run hotter."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/zagitta-gpu-unlock/speed-up-local-llm-prefill-on-an-rtx-3090/",
 "md": "https://dedupedai.com/use-cases/zagitta-gpu-unlock/speed-up-local-llm-prefill-on-an-rtx-3090/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--speed-up-local-llm-prefill-on-an-rtx-3090.json",
 "api_version": "v1"
}