GPU-unlock: FP/BF16 instruction issue rate on Nvidia 3000's cards
https://github.com/Zagitta/gpu-unlockHappy to answer questions!
https://github.com/aikitoria/open-gpu-kernel-modules
The hardware supports it, but then Nvidia disabled it if the driver detects cheaper cards. This bypasses the NVidia slowdown.
- you will need to run your card hotter than you might be comfortable with
- the real pay-off (on some cards, apparently, see downthread) requires increasing the maximum power to 450W
- This is well above what the stock cooler was designed for so a bit more cooling would help
What's nice is that you can stack this mod on top of (or rather, below of, this one loads first) the p2p mod that has been out for a while, they don't seem to interfere with each other.
> Unfortunately for Nvidia the booter is vulnerable to a classic buffer overflow that writes straight into the stack which gives us the ability to execute code via Return Oriented Programming (ROP).
Repo for RTX 4090 and 4070 Ti Super: https://github.com/JustAResearcher/gpu-unlock-ada
This unlock is super useful as it improves the GPU performance on computation-bounded neural network workloads like LLM prefilling, image/video generation, and general neural network training, in a way of disabling deliberate software segmentation on the same hardware imposed by Nvidia.
I'm on rtx 50 series, and no free performance uplifting for now. Waiting for more reverse engineering efforts to reveal whether the unlock is possible for 50 series.
However, it required a few extra steps, as the repo is set up for Linux kernel 6.x and I'm on 5.15. Claude Code was able to get me past the hurdles, including code modifications and startup scripts.
Right now, I'm clearly power limited at 170 watts and I have no desire to brick my card or set it on fire.
This is a benchmark test with Qwen 27B Int4 with 4x3090 and using full batching on VLLM to squeeze every bit of the GPU's.
On the other hand, this only applies to a limited set of workloads (training is often memory bandwidth bound for example).
I also wonder how this affects power consumption.