Zagitta/gpu-unlock

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Generation	Cards	Status
Ampere (RTX 30)	RTX 3050, 3060, 3060 Ti, 3070, 3070 Ti, 3080, 3080 Ti, 3090, 3090 Ti, plus RTX 3070/3080 Laptop GPUs	Stable, 100+ user reports
Ada Lovelace (RTX 40)	RTX 4060, 4060 Ti, 4070, 4070 SUPER, 4070 Ti, 4070 Ti SUPER, 4080, 4080 SUPER, 4090, 4090 D, RTX 4050/4060 Laptop GPUs	Experimental
Turing (RTX 20)	RTX 2060, 2060 SUPER, 2070, 2070 SUPER, 2080, 2080 SUPER, 2080 Ti, Titan RTX	Experimental
Blackwell (RTX 50)		Not supported

Card	BF16 before	BF16 after	Gain
RTX 3060 12 GB	27 TFLOPS	51 TFLOPS	+87%
RTX 3060 Ti	37 TFLOPS	68 TFLOPS	+82%
RTX 3070	40 TFLOPS	72 TFLOPS	+81%
RTX 3080 10 GB	65 TFLOPS	113 TFLOPS	+73%
RTX 3090	75 TFLOPS	125 TFLOPS	+65%
RTX 3090 Ti (450 W)	88 TFLOPS	157 TFLOPS	+80%
RTX 4070 Ti	81 TFLOPS	150 TFLOPS	+84%
RTX 4080	104 TFLOPS	193 TFLOPS	+87%
RTX 4090	165 TFLOPS	329 TFLOPS	+100% (peak MMA rate)

Workload	Typical speedup
LLM prefill / prompt processing, FP16/BF16 or AWQ/GPTQ (vLLM, PyTorch, Transformers)	+25–55%
Time to first token	25–30% lower
LoRA and full fine-tuning in BF16	+25–50%
Image generation, FP16/BF16 (SDXL, SD 1.5, Krea, DiT models in ComfyUI)	+30–55%
Video generation, BF16 compute (Wan 2.2)	+15–20%
FlashAttention / SDPA kernels	+50–60%
llama.cpp / Ollama with quantized GGUF	0–15%, mostly at long context
LLM decode / token generation (memory bound)	No change
INT8, FP8 and INT4 models	No change
Gaming and FP32 CUDA	No change

Supported GPUs