GPU Unlock Lifts Tensor Core Limits on NVIDIA RTX GPUs
Community projects led by Zagitta’s GPU Unlock modify GPU startup configuration to lift the FP32-accumulate instruction-rate restriction on FP16/BF16 Tensor Core operations in selected NVIDIA GeForce RTX 20-, 30-, and 40-series cards. Tests reported by Tom’s Hardware and 36Kr AI show peak matrix throughput nearly doubling in some cases, while Ampere LLM prefill improved 14–36% but decoding and INT8 performance stayed about the same. Linux implementations are available, while Windows support and coverage across specific card models remain experimental or incomplete.
The projects may give some local-AI and image-generation users more compute from existing RTX hardware, but the reported gains vary by card and workload and do not establish faster LLM decoding or improved gaming performance.
Basically saw on twitter claims that 3090 has half of the cuda kernels disabled:
My 3090s just got 1.9x FASTER at BF16 tensor math!!! Same cards, same driver, same 300W, one Linux kernel module.
bf16 tensor pipe: 82 -> 155 TFLOPS cuBLAS BF16 GEMM 8192: 64 -> 99 TFLOPS TF32: 41 -> 79 TFLOPS
NVIDIA ships every 3090 with fp32-accumulate tensor math at half speed. The driver even reports it: FMLA32 = 1/2. gpu-unlock by Zagitta sets it to full before the NVIDIA driver loads. After a cold boot all 4 of my cards say FMLA32 = full.
Real inference got less than I predicted. Strata, Qwen3.8 Flash-Next on 2 cards, prefill: 4K: +12% 10K: +12% 90K: 3205 -> 3536 tok/s, +10%
Before installing I said +14 to 19% at 90K, so I was wrong. The GEMM phases got 23% shorter, but they are only about 40% of Strata prefill, and the int8 experts, copies and CUDA core kernels didnt get any faster.
Outputs did not change. With the same expert cache size, my log-prob dump over 1996 teacher-forced positions is the same file byte for byte as the stock run before the module.
Wondering if it will work for 40 series cards
submitted by /u/UltramanQuar [link] [comments]
#GPU Unlock#NVIDIA GeForce RTX 20 Series#NVIDIA GeForce RTX 30 Series#NVIDIA GeForce RTX 40 Series#NVIDIA GeForce RTX 3090#NVIDIA GeForce RTX 3090 Ti#NVIDIA GeForce RTX 3080 Ti#NVIDIA GeForce RTX 4090#NVIDIA GeForce RTX 2080 Ti#Zagitta