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Zagitta 开源 gpu-unlock 内核模块,将 RTX 30 系 FP/BF16 指令发射率解锁到专业卡水平

原标题:Zagitta/gpu-unlock: Unlock FP/BF16 instruction issue rate on Nvidia 3000's cards to their professional equivalents

GitHub 开源仓库 Zagitta/gpu-unlock 通过 Linux 内核模块,在 NVIDIA 驱动加载前解锁 GeForce 30 系(GA102/103/104/106)的 FP/BF16 指令发射率,使其达到专业卡水平。该模块结合 CMP 解锁器的 V67/PLM 技术与 UEFI 方式的预驱动时序,写入速度选择器(SS0 0x88888888 与 4 位 SS1 0x8 IMLA4)并执行 FLR,无需随 NVIDIA 新驱动版本手动重打补丁。用户实测 BF16 GEMM 提升约 +25–80%,RTX 3090 Ti 稠密 GEMM 的 BF16 从 87.50 TFLOPS 升至 157.41 TFLOPS;LLM prefill 提速 +14–36%,扩散模型与 FlashAttention 也受益,受内存带宽限制的 LLM decode 和 INT8 负载基本…

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仓库作者本人说明了解锁原理、安装方法和多卡实测数据,对需要在老卡上加速矩阵计算的用户可直接参考。

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原文

GitHub 上的新仓库 Zagitta/gpu-unlock,创建 7 天,已有 3628 星(平均每天 505 星)。 Unlock FP/BF16 instruction issue rate on Nvidia 3000's cards to their professional equivalents 语言:C

README:

GPU unlock 1.0.0

Unlock FP/BF16 instruction issue rate on Nvidia 3000's cards to their professional equivalents

This Linux kernel module combines the CMP unlocker's V67/PLM technique with the pre-driver timing of the UEFI approach. 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 FLR before the unmodified NVIDIA driver binds the card. It is neither a UEFI application nor a patched NVIDIA driver.

The benefit of this early kmod driver is that it does not require manual patching each time nvidia releases a new driver version.

The effect of this unlock is that it doubles the instruction issuing speed of FP/BF16 FMA into FP32 operations on GA102, GA103, GA104 and GA106 cards.

3090 Ti dense 8192³ GEMM at 450 W, five-sample medians with FP32 accumulation:

Input Before After
FP16 87.46 TFLOPS 118.39 TFLOPS
BF16 87.50 TFLOPS 157.41 TFLOPS
TF32 42.58 TFLOPS 82.61 TFLOPS

Tested cards

Results reported by users. The module accepts every GeForce ID listed in kernel/gpu_unlock/main.c, but the cards below are the ones someone has reported on. Please add yours with a compatibility report, whether it worked or not.

Chip Card PCI ID Result BF16 GEMM gain Reports
GA102 RTX 3090 Ti 10de:2203 Unlocked +68–80% #1
GA102 RTX 3090 10de:2204 Unlocked +25–67% #5, #9, #10, #17, #25, #27, #33
GA102 RTX 3080 Ti 20 GB (OEM, GA102-250) 10de:2205 Unlock completes, BF16 stays at half rate — #28
GA102 RTX 3080 10 GB 10de:2206 Unlocked +70–73% #14
GA102 RTX 3080 20 GB (memory upgrade) 10de:2206 Unlocked +56–59% #10, #15
GA104 RTX 3070 10de:2484 Unlocked +73–81% #10, #12
GA104 RTX 3070 Ti 10de:2482 Not tested with this module (the same unlock worked from a UEFI port on Windows) — #32
GA106 RTX 3060 12 GB 10de:2504 Unlocked about +80% #13, #31
GA103 any 10de:2414 No reports yet — —

The gains are dense GEMM throughput from each report's own benchmark, so they are not directly comparable. The power limit matters a lot: the lowest figure (+25%) is from cards capped at 225 W.

Matrix-heavy work speeds up: LLM prefill (+14–36% in #9, #15, #25 and #33), diffusion and FlashAttention. LLM decode, which is limited by memory bandwidth, and INT8 work stay about the same. The reports that compared outputs found them bit-identical before and after.

Reports also cover up to four cards in one machine, the card driving the display, and cards passed through to a VM with vfio-pci (#17).

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:

  • GRUB (Debian/Ubuntu/Mint): add it inside GRUB_CMDLINE_LINUX_DEFAULT in /etc/default/grub, then run sudo update-grub.
  • GRUB (Arch/CachyOS): same edit, then run sudo grub-mkconfig -o /boot/grub/grub.cfg.
  • Fedora/RHEL: run sudo grubby --update-kernel=ALL --args="gpu_unlock.targets=all".
  • systemd-boot: append it to the options line in your boot entry (bootctl status shows its location).
  • UKI / generated entries: add it to /etc/kernel/cmdline and regenerate your boot images/entries (e.g. sudo mkinitcpio -P for mkinitcpio UKIs).

Local build

Every build downloads the pinned NVIDIA/CachyOS inputs over HTTPS, checks their SHA-256, 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 committed HEAD.

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    #

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事件综述

开源工具解锁NVIDIA RTX 30系硬件指令性能进展综述

近期,围绕开源的 `gpu-unlock` 内核模块,针对 NVIDIA GeForce 30 系列消费级显卡(GA102/103/104/106)的硬件限制进行解锁的工作取得了快速进展。该模块通过 Linux 内核模块方式,结合 UEFI 预驱动时序,在不依赖 NVIDIA 新驱动版本的情况下,旨在解锁 FP/BF16 等指令发射率。 **针对 RTX 3090/3090 Ti 的进展方面:** 最初发布于 2026年10月21日的 1.0.0 版本,在 RTX 3090 上实测提升了约 15% 的 PP 和 5% 的 TG。后续的跟进报道指出,BF16 tensor 运算能力得到巨大提升,如 3090 的 BF16 GEMM 从 64 升至 99 TFLOPS,而 3090 Ti 在 BF16 GEMM 上的性能从 87.50 TFLOPS 提升至 157.41 TFLOPS。但在实际 LLM 推理场景中,其对 2 卡上 90K token prompt 的提速约在 10%左右,部分提升受 GEMM 阶段缩短比例有限。 **针对 RTX 3080 20GB 的进展方面:** 解锁的范围进一步扩展至 20GB 版本的 RTX 3080。技术实现上存在两种方法:一是原作者的补丁,通过解锁 ReBAR 和 P2P 特性,在双卡 Qwen3.8 27B 模型测试中,单流 prefill 速度提升达到 71%至 115%(8192 token prompt 从 967 tok/s 提升至 2080 tok/s)。二是用户利用 DeepSeek AI 指令辅助解锁底层 Tensor Core 功能,实现 FP16 性能从 60.54 TFLOPS 的提升至 109.39 TFLOPS,约 80.7% 的性能飞跃。 **技术原理与局限性:** 解锁工具利用了 2026年6月披露的 Booter 缓冲区溢出漏洞,以最高权限覆盖寄存器恢复全速,旨在消除消费卡被工厂熔断限制为一半张量核心速度的状态。该技术可适用于 Turing、Ampere 和 Ada 架构,但目前指出,由于 Blackwell 采用 FSP(RISC-V 根信任)启动且无可利用的 Booter,故目前无法实现解锁。

其他来源的报道 2