GPU-unlock: FP/BF16 instruction issue rate on Nvidia 3000's cards

Hey this is my work, although the exploits etc aren't my ideas by any means. This is just a way to wrap the whole thing up in a much less annoying package than having to patch the nvidia driver. I originally got inspired by the CMP90/50 windows unlocks that run as standalone UEFI modules that chainloads into the OS.

Happy to answer questions!

A semi related: a patch for those with multiple cards. You can unlock around 20% increased card to card speed on consumer GPUs using this library:

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.

This one is real, but there are some caveats:

- 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.

I've tested myself and the results are legit, this is incredible, even if a little risky (it relies on exploiting firmware): https://github.com/Zagitta/gpu-unlock/blob/main/docs/How%20A...

> 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

So if I'm reading this chart correctly, my 3090 card can get a 65-80% boost in speed generally? Or, is this only for certain models and quantizations?

As a person who uses consumer level hardware to do neural network research, this unlock is truly INVALUABLE.

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.

Where's the 5090 version?

It worked for me, and I'm getting about 6% faster training on nanochat with my 3060 card. Thanks for this!

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.

The real gains are far less than the advertised. Tests ton TFTOPS show 25% gains at 200Watt cap. While mostly prefil benefit from that less decoding which make the real gains 7-8%. Using at full power:

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.

The next version is sure to have signed firmware which sets these configurations to prevent this sort of thing.

Really interesting.

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.

Honestly if Nvidia is going to be short on cards for years there is no reason to keep this locked. People are going to buy what they can get and this in no way is going to influence sales at this point by making it official.

cool

Uh, no. This headline is wrong. It's FP32 unlocked from being halved, not 16 bit ops. And to be clear, over the last week I've looked many places and I've not seen any reports of this unlock being applied to any 3xxx card except the 3090/3090ti series. Even if it might apply more generally in theory.

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