# gpu-unlock-ada: Enable Tensor Speed-Select on Ada GPUs

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A Linux kernel module that uses the AD102 payload from TOTORONG/AD103_win_unlocker to enable an SM speed-select override before NVIDIA’s driver loads. It targets RTX 4090 and opt-in experimental Ada GPUs, with locally generated firmware assets.

GitHub: https://github.com/JustAResearcher/gpu-unlock-ada
OSRepos URL: https://osrepos.com/repo/justaresearcher-gpu-unlock-ada

## Summary

A Linux kernel module that uses the AD102 payload from TOTORONG/AD103_win_unlocker to enable an SM speed-select override before NVIDIA’s driver loads. It targets RTX 4090 and opt-in experimental Ada GPUs, with locally generated firmware assets.

## Topics

- c
- linux
- performance
- kernel-module
- gpu-unlocking
- tensor-computing

## Repository Information

Last analyzed by OSRepos: Tue Oct 27 2026 13:53:24 GMT+0000 (Western European Standard Time)
Detail views: 2
GitHub clicks: 0

## Safety Notice

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## Content

## Overview

`gpu-unlock-ada` is a Linux kernel module that integrates the AD102 payload from TOTORONG’s `AD103_win_unlocker` into the Linux module and firmware-generation base from Zagitta’s `gpu-unlock`. It enables an SM speed-select override before the stock NVIDIA driver binds, addressing reduced tensor instruction throughput on supported GPUs without requiring a patched NVIDIA driver or UEFI application.

The primary target is the RTX 4090; other Ada GPU IDs are available only as explicit experimental opt-ins, with evidence varying by model. This is a hardware-level change, not a general-purpose GPU performance tool: measured instruction throughput gains do not necessarily translate to application gains. The README also describes a separate Windows reference port for one MSI RTX 4070 Ti, but that does not add Windows support to the Linux module.

## Key Features

- Runs as a Linux kernel module early in boot, before the NVIDIA driver binds.
- Selects the AD102 firmware and payload automatically for RTX 4090 devices.
- Supports selected experimental Ada IDs through individual opt-in flags and, for some models, exact PCI-address selection.
- Builds firmware assets locally from pinned NVIDIA inputs and verifies asset hashes before use.
- Checks the payload completion marker, permissions and speed-select readout, including after PCIe function-level reset.
- Includes host-side tests and optional CUDA instruction benchmarks, plus published hardware reports and testing guidance.
- Provides package and initramfs integration for mkinitcpio, initramfs-tools and dracut.

## Use Cases

- RTX 4090 owners who want to test higher FP16 or BF16 tensor instruction throughput on Linux and can validate the result on their specific card.
- Owners of listed experimental Ada GPUs who are prepared to opt in by exact device and assess the model-specific evidence before deployment.
- GPU researchers investigating tensor instruction rates and firmware-level speed selection, rather than expecting equivalent gains in every workload.
- Linux system builders who want an early-boot module and locally generated firmware assets instead of a patched NVIDIA driver or UEFI application.

## Our Take

This is a narrowly scoped, actively maintained hardware experiment with meaningful validation, but its results and safety depend on the exact GPU and boot state.

**Good fit if:**
- You have a supported Linux GPU and can follow the cold-boot, driver-ordering and post-boot verification steps.
- Your goal is to investigate FP16/BF16 or other measured tensor instruction rates, and you understand that application performance may differ.
- You can build the module and firmware assets locally and accept GPL-2.0-only obligations for the covered project code.

**Look elsewhere if:**
- You need a broadly supported performance improvement for an unlisted GPU, or a guaranteed gain across inference and other application workloads.
- You need Windows support from this repository; its Windows reference is a separate, single-profile contribution, not Linux-module target support.
- You cannot validate GPU health and recover from a boot or firmware-state problem on the machine you plan to modify.

## Project Health

| Signal | Value |
|---|---|
| Status | **New**: less than six months old and changing fast |
| Last commit | 2026-10-26 (1 day ago) |
| Commits, last 90 days | 100+ |
| Releases, last 12 months | 2 (latest v1.1.1, 2026-10-24) |
| Contributors | 20 (top contributor: 43% of commits) |
| Issues closed, last 90 days | 33 (typically closed in 1 day) |
| Pull requests merged, last 90 days | 23 (typically merged in 0 days) |
| Project age | 3 days |

Checked on 2026-10-27 with the GitHub API.

## Project Facts

- Language: C
- License: GPL-2.0
- Stars: 4.1k
- Forks: 538
- Archived: no

## What You Need

Detected in the repository:

- C (main language on GitHub)
- A test suite

## Getting Started

```sh
git clone https://github.com/JustAResearcher/gpu-unlock-ada.git
cd gpu-unlock-ada
make install
```

Review the README’s supported-target table and boot instructions before enabling the module. Build inputs and firmware are generated locally; consult the README for distribution-specific targets, kernel command-line options and validation steps.

## License in Plain Words

**GPL-2.0** (strong copyleft).

- Commercial use: yes
- Modify and redistribute: yes
- You must keep: the license notice
- Share your changes: yes, the full source of anything you distribute that includes it, under the GPL

A summary, not legal advice: the LICENSE file is what applies.

## Considerations

The repository is very new and changing quickly, although it has recent releases, frequent commits and issue and pull-request activity. Contributions are not dominated by one person, but the short project history limits what that says about long-term maintenance or hardware coverage.

An unlock requires a compatible GPU, Linux kernel headers, a working DKMS toolchain, locally generated firmware assets and correct initramfs ordering. The first attempt requires a cold start, and a warm reboot may not restore the hardware state needed to rerun the firmware sequence. Experimental Ada support is explicitly limited to listed IDs and small numbers of tested cards; laptop results in particular may not carry across models. Reported instruction-rate gains do not ensure faster real workloads, and some measured workloads showed no benefit. The module, packaging and payload integration are GPL-2.0-only; NVIDIA firmware remains subject to its own terms.