Zagitta/gpu-unlock

★ 13,786⑂ 1,349

Your GeForce runs AI at half speed. This fixes it for 20/30/40 series cards. Up to 2× FP16 / BF16 / TF32 tensor throughput

About Zagitta/gpu-unlock

Zagitta/gpu-unlock is an open-source project on GitHub, mainly written in C. Your GeForce runs AI at half speed. This fixes it for 20/30/40 series cards. Up to 2× FP16 / BF16 / TF32 tensor throughput It currently holds 13,786 stars and 1,349 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).

Project Overview

AI Homed tracks it on the Today's Trending board.

GitHub Repository Details

Repository Zagitta/gpu-unlock · default branch - · size 0 KB · watchers 0 · source: GitHub REST API and repository README

README

gpu-unlock: full-speed tensor cores for GeForce RTX

Your GeForce runs AI at half speed. This fixes it.

VersionRTX 20 | 30 | 40LinuxLicense GPL-2.0Stars

Up to 2× FP16 / BF16 / TF32 tensor throughput · +25–55% LLM prefill · +25–50% fine-tuning · +30–55% image generation

Supported GPUs · Benchmarks · Install · FAQ · Support

---

NVIDIA ships GeForce cards with their tensor cores capped to half speed for FP16, BF16 and TF32 math with FP32 accumulation, which is what nearly all AI training and unquantized inference uses. The same silicon sold as an RTX A-series or professional card runs at full rate. gpu_unlock removes that cap at boot, before the stock NVIDIA driver loads. No patched driver, no VBIOS flash, nothing to redo when NVIDIA releases a new driver.

---

Supported GPUs

| Generation | Cards | Status | | ------------------------- | ---------------------------------------------------------------------------------------------------------------------- | ------------------------- | | Ampere (RTX 30) | RTX 3050, 3060, 3060 Ti, 3070, 3070 Ti, 3080, 3080 Ti, 3090, 3090 Ti, plus RTX 3070/3080 Laptop GPUs | Stable, 100+ user reports | | Ada Lovelace (RTX 40) | RTX 4060, 4060 Ti, 4070, 4070 SUPER, 4070 Ti, 4070 Ti SUPER, 4080, 4080 SUPER, 4090, 4090 D, RTX 4050/4060 Laptop GPUs | Experimental | | Turing (RTX 20) | RTX 2060, 2060 SUPER, 2070, 2070 SUPER, 2080, 2080 SUPER, 2080 Ti, Titan RTX | Experimental | | Blackwell (RTX 50) | | Not supported |

Laptops only stay unlocked if the dedicated GPU is never fully powered off. Many laptops power it down when idle, which silently resets the unlock. Turning off runtime power management with options nvidia NVreg_DynamicPowerManagement=0x00 in /etc/modprobe.d/ helps on some models, but expect mixed results.

The exact PCI device IDs are in kernel/gpu_unlock/recipes.c. Linux only. For Windows see the FAQ.

---

What you get

The tensor core rate doubles. How much of that you see depends on how much of your workload is FP16/BF16/TF32 matrix math, and on whether the card has the power headroom to run it.

Dense matrix multiply (FP32 accumulation, stock power limits):

| Card | BF16 before | BF16 after | Gain | | --- | --- | --- | --- | | RTX 3060 12 GB | 27 TFLOPS | 51 TFLOPS | +87% | | RTX 3060 Ti | 37 TFLOPS | 68 TFLOPS | +82% | | RTX 3070 | 40 TFLOPS | 72 TFLOPS | +81% | | RTX 3080 10 GB | 65 TFLOPS | 113 TFLOPS | +73% | | RTX 3090 | 75 TFLOPS | 125 TFLOPS | +65% | | RTX 3090 Ti (450 W) | 88 TFLOPS | 157 TFLOPS | +80% | | RTX 4070 Ti | 81 TFLOPS | 150 TFLOPS | +84% | | RTX 4080 | 104 TFLOPS | 193 TFLOPS | +87% | | RTX 4090 | 165 TFLOPS | 329 TFLOPS | +100% (peak MMA rate) |

The RTX 4090 runs out of power before it runs out of tensor cores, so large GEMMs at stock power limits gain much less than the peak rate suggests.

Real workloads:

| Workload | Typical speedup | | --- | --- | | LLM prefill / prompt processing, FP16/BF16 or AWQ/GPTQ (vLLM, PyTorch, Transformers) | +25–55% | | Time to first token | 25–30% lower | | LoRA and full fine-tuning in BF16 | +25–50% | | Image generation, FP16/BF16 (SDXL, SD 1.5, Krea, DiT models in ComfyUI) | +30–55% | | Video generation, BF16 compute (Wan 2.2) | +15–20% | | FlashAttention / SDPA kernels | +50–60% | | llama.cpp / Ollama with quantized GGUF | 0–15%, mostly at long context | | LLM decode / token generation (memory bound) | No change | | INT8, FP8 and INT4 models | No change | | Gaming and FP32 CUDA | No change |

Outputs are bit-identical before and after; only the speed changes. These numbers come from the compatibility reports users have filed, where you can find full benchmark details and post your own.

---

How to install

First run:

git clone https://github.com/Zagitta/gpu-unlock
cd gpu-unlock
make install

Then append gpu_unlock.targets=all to your kernel command line, keeping existing options:

in /etc/default/grub, then run sudo update-grub. sudo grub-mkconfig -o /boot/grub/grub.cfg. sudo grubby --update-kernel=ALL --args="gpu_unlock.targets=all". (bootctl status shows its location). your boot images/entries (e.g. sudo mkinitcpio -P for mkinitcpio UKIs).

FAQ

A: No this is the one and only OG source for gpu-unlock. Crypto, websites, installers and what else is not associated with this project or the developer in any way shape or form. A: No the driver is only fetched to extract firmware this unlock depends on A: I don't run windows and don't plan to maintain it but https://github.com/impacifist/Windows-GPU-UNLOCK has built a port, YMMV. There are windows installer's floating around but I would recommend being ++very++ careful with those. A: Not supported and unlikely to happen. A: Market segmentation, they want businesses to purchase the professional cards without these limitations at significantly higher prices. A: It's very unlikely, this only unlocks a small handicap Nvidia has put in place to differentiate datacenter and professional cards from consumer versions. It may heat up your GPU slightly because it now does more compute but the standard throttling and power management remains completely intact and should downclock. A: Please consider donating so I can purchase more GPUs <3

Local build

Every build downloads the NVIDIA/CachyOS inputs over HTTPS and generates firmware only in the build directory and the binary package. All three formats install through the same make install-* targets. Packages are built from the working tree, uncommitted changes included.

Build, install build dependencies, and install the packages in one step (asks for your sudo password):

make install        # detect the distribution and select an installer
make install-arch   # makepkg -sif
make install-deb    # apt-get build-dep, dpkg-buildpackage, apt-get install
make install-rpm    # dnf builddep, rpmbuild, dnf install

make install uses /etc/os-release (ID, then ID_LIKE) to detect Arch, Debian/Ubuntu, or Fedora/RHEL/CentOS families. The RPM installer requires DNF. For other distributions, select a compatible explicit target above.

make arch, make deb and make rpm only build. Debian and RPM packages go to build/deb and build/rpm. The NVIDIA inputs are cached in ~/.cache/gpu-unlock/inputs (make inputs; override with INPUTS=). dpkg-buildpackage itself never uses the network, so a clean-chroot build (sbuild or pbuilder) needs GPU_UNLOCK_INPUTS pointing at that cache. The make dist archive, the Debian source package and the .nosrc.rpm contain no NVIDIA archives or generated firmware.

How it works

This Linux kernel module combines the cmpunlocker's V67/PLM technique with the pre-driver timing of the UEFI approach. At boot it runs the unlock, writes the speed selectors (SS0 0x88888888, which selects the full BF16 rate, and the 4-bit SS1 0x8 IMLA4) and performs an FLR before the unmodified NVIDIA driver binds the card. It is neither a UEFI application nor a patched NVIDIA driver.

Boot flow and opt-in

The packages put gpu_unlock and its firmware in the initramfs and load it there, before nvidia can bind a card:

gpu_unlock right before nvidia in MODULES, so a display card is blank for as short a time as possible. Without nvidia in the image, it goes last and still runs before an nvidia loaded from the root filesystem. Images are rebuilt on install. mkinitcpio ignores drop-ins for an image built with -c or a preset _config; put gpu_unlock right before nvidia in that config's MODULES yourself. With initramfs-tools and dracut, the module is built for every installed kernel that has headers, and those kernels' images are rebuilt on install and again on removal. Kernels installed later get the module and a new image from the kernel's own install hooks.

There is no softdep nvidia pre: rule: kmod honours only the first softdep nvidia line it reads, so other packages' rules (nvidia-utils, gddr6_hwmon) would silently override it, or it would override theirs.

The module does nothing unless the kernel command line opts in:

gpu_unlock.targets=all                          # every supported unbound card
gpu_unlock.targets=0000:21:00.0                 # only these cards
gpu_unlock.targets=0000:21:00.0,0000:22:00.0    # comma-separated

Addresses are full PCI names as lspci -D prints them (hex digits in either case), independent of GPU numbering and display role. An entry that is not a supported card (see Supported GPUs), including all mixed with addresses, is logged and ignored. The cards are unlocked concurrently, about a second in all however many there are, and each on its own: a card that is already bound or powered down is skipped. Bus mastering that the board firmware left on (Supermicro X11 does this whatever its option ROM and bus master settings) is cleared first; a card some driver already initialised is still refused. Then nvidia loads and binds every card as usual.

The display card works too. Its firmware framebuffer (the boot console and Plymouth) keeps running while the unlock's firmware steps run, and is removed just before the card's reset, as nvidia-drm would do a moment later anyway. Its screen is blank from that reset until nvidia-drm takes over.

Loading the module by hand after boot does nothing to a card that nvidia has bound.

Suspend to RAM powers the cards off, which resets the speed selectors. After resume the module unlocks every card it unlocked at boot again, before nvidia resumes it: its suspend notifier runs just ahead of the one nvidia resumes its cards from (NVreg_UseKernelSuspendNotifiers=1, the default in current drivers; nvidia-resume.service runs later still). This adds about a second to resume. A card that kept power, as in suspend to idle, is still unlocked and is only read. A card that nvidia has already resumed, or that is bound to another driver, is left as it is, and the log says why. Add gpu_unlock.resume=0 to the kernel command line, or write 0 to /sys/module/gpu_unlock/parameters/resume, to turn this off. Hibernation is not covered.

Check the result with:

journalctl -k -b | grep gpu_unlock
lspci -nnk -s 0000:[ID]:00.0

To stop unlocking, remove gpu_unlock.targets= from the kernel command line; for a one-off boot, delete it in the bootloader's edit screen. Removing the packages also removes the module and the initramfs drop-ins.

If the firmware on a card never stops, the module logs it and keeps the DMA buffers that firmware may still use until the next power off. Power off before trying again.

The kernel module and local packaging are GPL-2.0-only. The generated buffer derives from the pinned CMP reference; NVIDIA firmware remains under its own terms. See firmware/NOTICE.md and licenses/ for attribution and notices.

Support me

Help me buy more GPUs

GitHub Stars & Activity

13,786Stars
1,349Forks
0Open issues
CLanguage

GitHub Popularity

GitHub stars13,786
Forks1,349
Open issues0
Primary languageC
License-
Stars gained today0
Created-
Last pushed-

Trending History

Monthly boardrank #96 · ▲ 0 stars

Related AI Projects

1

obra / superpowers

Shell★ 309,942⑂ 27,571▲ 663 stars
→
2

mattpocock / skills

Shell★ 308,196⑂ 25,934▲ 882 stars
→
3

anthropics / skills

Python★ 185,808⑂ 21,939▲ 756 stars
→
4

DietrichGebert / ponytail

JavaScript★ 181,437⑂ 9,980▲ 621 stars
→
5

f / prompts.chat

HTML★ 175,423⑂ 22,393▲ 213 stars
→
6

msitarzewski / agency-agents

Shell★ 165,662⑂ 26,818▲ 334 stars
→
7

Graphify-Labs / graphify

Python★ 139,947⑂ 13,346▲ 686 stars
→
8

ggml-org / llama.cpp

C++★ 132,980⑂ 25,006▲ 120 stars
→

More AI Rankings