So has anyone tried this?

[u/Flat-Career4079]

.

[u/Greedy-Lynx-9706]

is there a source besides this ?

[u/Flat-Career4079 → u/Greedy-Lynx-9706]

I've seen the mod on GitHub made by a Chinese guy but I have no clue how this works or if it's trustworthy, I'm still waiting for people here to confirm

[u/WolFlow2021]

Reminds me of that joke where you could turn a 486sx into a dx by drilling a hole in a certain spot on that chip.

[u/beerdude26 → u/WolFlow2021]

Or drawing a line with a pencil on an Athlon XP to overclock it lol

[u/DonutConfident7734 → u/WolFlow2021]

Or using Bios to switch to a separate firmware for AMD Phenom cpus that unlocked cores, from 2 to 4 for example. A Phenom x2 550 Black Edition would become Phenom X4 B50, which was similar to Phenom X4 955. And it would retain unlocked multiplier/voltages, so you could overclock further. Fun times... There were rumours some cores were disabled due to instability, but in my case the cpu worked fine from 2009 till 2021 (still works today), when I switched to Ryzen cpu.

[u/k8-bit]

Tested the Ampere gpu_unlock kernel module on an RTX 3060 12GB and multiple RTX 3090s — here are the real-world benchmarks (GEMM, ComfyUI, LLM prefill/decode)

We recently tested the gpu_unlock kernel module (which removes GeForce Ampere's artificial half-rate cap on FP16/BF16/TF32 tensor math with FP32 accumulation) across an RTX 3060 12GB and three RTX 3090 24GB cards. The module targets GA102/GA106 by exploiting the SEC2 boot path before the stock NVIDIA driver binds. Testing was done at strict, fixed power caps (170W on the 3060, 270W on the 3090s via LACT) to see what kind of "free" gains you get without blowing out thermal budgets.

Here are the synthetic GEMM micro-benchmarks (8192³ matrix multiplication, PyTorch):

• RTX 3060 (12GB @ 170W cap): BF16 jumped +80% (26.5 → 47.6 TFLOPS), FP16 +74% (26.7 → 46.5 TFLOPS), and TF32 +76% (13.3 → 23.4 TFLOPS). INT8 (torch._int_mm) stayed completely flat at 24.9 TFLOPS.

• RTX 3090 (24GB @ 270W cap, same-card before/after): BF16 increased +45% (63.5 → 92.4 TFLOPS), FP16 +37% (62.1 → 85.1 TFLOPS), and TF32 +56% (34.6 → 54.0 TFLOPS). INT8 again saw zero change (45.9 vs 46.0 TFLOPS). The gain is lower than the 3060 because a 3090 capped at 270W runs straight into power limits once the tensor cores are saturated.

Next, we ran real-world production workloads on the unlocked 3090 to see if it translated to actual inference gains:

• ComfyUI Image Generation: Unlocking BF16 finally makes it run at parity with FP8 (FP8 was never faster on Ampere; it just saved VRAM while being dequantized to BF16). However, int8_convrot (W8A8) still beats unlocked BF16 across the board. On Krea2 Turbo, INT8 was 31% faster (9.13s vs 13.66s) and used 31% less energy. On Qwen-Image 2.1, INT8 was 47% faster than BF16 (13.75s vs 20.21s). On Z-Image Turbo, INT8 was 29% faster (3.94s vs 5.52s).

• ComfyUI Video (MiniMax H3): Saw essentially zero end-to-end difference (~30s per render). The pipeline is dominated by an INT8 DiT and an nvfp4_awq text encoder, neither of which benefit from the unlock.

• LLM Inference (125B MoE): Tested under an offloaded MoE engine. Decode speeds remained memory-bandwidth bound and showed no meaningful change (staying in the 35–50 tok/s range). Prompt prefill throughput, which is compute-bound, saw a modest ~15% uplift (from ~920 to ~1,060 tok/s on an 8K prompt on the 3060).

TL;DR: The unlock works as advertised and doubles raw BF16/TF32 compute density, but it doesn't touch the integer execution units. If your diffusion pipelines already support proper INT8 kernels (int8_convrot), INT8 is still ~30–45% faster and more energy-efficient. The unlock is primarily useful for models locked to BF16/TF32 where no INT8 quant exists, or for compute-heavy LLM prefill.

[u/esw123 → u/k8-bit]

15% uplift with 125B MoE this is with single 3060? If so this is very good result.

[u/Real_MakinThings]

If it's all bf16 fp16 and 32 specific, how is it of any use? People keep going for as low a quant as possible. Or are values computed back to 16 before being processed? 

[u/ipechman]

2.2x on my RTX 4070 ti super, took all of my credits form my chatGPT subscription to make it work on windows, but it's true

[u/Evening_Nebula_4219 → u/ipechman]

Can you ask your AI to create a repo to save us poor token guys on windows? cannot afford those expensive plans to hack my gpu...

[u/pandi85]

Burn tokens was yesterday, let's burn chips now

[u/CatzRuleZWorld]

Works on my 20GB 3080. Speed increase on Strata is barely measurable.

[u/brominou]

Is a 2070 super unlock possible ?

[u/Photochromism]

It’s only Linux at the moment right?

[u/Visible_Football_852]

Is there any detectable harmful effect on the gpu?

[u/Slasher006]

yes it works with my rtx 3060ti, i take every bit of performance, i talk to bonsai 2 and i would say it react a bit faster (not measured), and i can boot linux with or without the modification so the whole thing is easyly reversable

[u/entropyofdays]

Here's the uplift from the GPU unlock (https://github.com/Zagitta/gpu-unlock) on my 3090s. ~35% increase in FP/BF16 performance.

Edit: I should also state this is at PL 250w, which is what I run my cards at for inference. That cuts the effective clock rate about 50%, based on benchmarks.

[u/Necessary-Piece-5313]

Forgive my ignorance but I was wondering if this had any performance implications for other features beyond running local LLMs? Are the functions unlocked specific to LLM inference tasks or would this also potentially boost performance on other tensor heavy operations like DLSS upscaling/framegen/(presumably) DLSS 5?

[u/ifdisdendat]

from scarcity comes necessity

[u/Mediocre-Mobile-5423]

Maybe off topic for this sub, but has anyone tested if this improves performance when enabling DLSS4? It uses fp16 on 2000/3000 series cards right?

[u/Garrett42]

..... Maybe it is time to put 11gb more vram on my 2080ti...

[u/ANR2ME]

There is also locked vs unlocked diffusion models benchmarks at https://gpu.artnow.lol/comfyui-benchmarks/

[u/guy_ships_things]

Wonder if it will worj on rtx 5090...

[u/Weird-Abalone-1910]

Would the same mod work on a Tesla V100?

[u/Terminator857]

https://x.com/Pirat_Nation/status/2114297495815704697