Picture: Illustration · BitBytec · Original illustration, BitBytec
A new open-source UEFI tool boots from USB and unlocks significant extra tensor performance on RTX 20, 30, and 40-series desktop and laptop GPUs, making the half-speed AI throttle hack a whole lot easier to try on Windows [1]. The nv-uefi-unlocker project removes Nvidia’s tensor limiter using the community V67 payload with read-back verification [1].

Key facts * The nv-uefi-unlocker project covers RTX 20, 30, and 40-series desktop and laptop GPUs [1]. * The tool runs from a FAT32 stick before the Nvidia driver loads at every boot, requiring no VBIOS flash or patched driver [1]. * The developer’s own laptop RTX 4060 hit 65% higher FP16 math using this method [1]. * The tool allows the unlock to run entirely from a bootable USB drive on both Windows and Linux [1].
How does the nv-uefi-unlocker work? The nv-uefi-unlocker project tricks Nvidia’s signed Booter firmware into opening the write lock on two speed-selector registers, allowing the tensor units to run at full rate [1]. This process uses a community V67 payload with read-back verification to ensure the unlock is safer than previous experimental builds [1].
What are the limitations and requirements? Results from the unlock are contingent on power and thermal limits, and the project warns that combining it with the nvoc power-limit unlock "can exceed factory power and thermal envelopes" [1]. The project also notes that a per-card ROM hash check is not yet built, and the unlock’s persistence on Thunderbolt eGPUs is not guaranteed [1]. Additionally, the original creator of the gpu-unlock project, Zagitta, does not plan to maintain the Windows version and warns users to be careful with unofficial installers [1].
Sources
- Open-source USB tool unlocks Nvidia's half-speed GPU AI limiter on Windows — lifts GPU's FP16 math performance up to 65%, UEFI unlocker covers RTX 20, 30, and 40-series desktops and laptops — www.tomshardware.com (primary source)
- PsiQuantum turns its photonics know-how into an optical switch for supercharging AI data center GPU networks — 8x8 lab prototype loses less light than rivals, with plans to develop larger devices — www.tomshardware.com
Written with AI from the sources listed, checked against them before publishing.
