# "Has anyone else hit the physical TGP ceiling on the Mobile 4060? I wrote a PyTorch cold-boot script that sustains 26.3+ TFLOPS across massive 163K x **URL:** \ **Category:** CUDA Programming and Performance\ **Created:** [April 8, 2026, 2:14am UTC](https://forums.developer.nvidia.com/t/has-anyone-else-hit-the-physical-tgp-ceiling-on-the-mobile-4060-i-wrote-a-pytorch-cold-boot-script-that-sustains-26-3-tflops-across-massive-163k-x/365952 "2026-04-08T02:14:21Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![demonrice](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@demonrice](https://forums.developer.nvidia.com/u/demonrice)\ **Post date:** [April 8, 2026, 2:14am UTC](https://forums.developer.nvidia.com/t/has-anyone-else-hit-the-physical-tgp-ceiling-on-the-mobile-4060-i-wrote-a-pytorch-cold-boot-script-that-sustains-26-3-tflops-across-massive-163k-x/365952/1 "2026-04-08T02:14:21Z")
# (venv) PS C:\Users\watch\OneDrive\Desktop\veilsight\burst\_expirements\> python apex\_hunter.py # 🌌 VEILSIGHT APEX HUNTER: THE 28 TFLOPS BARRIER Target: Absolute Silicon Saturation (Cold-Boot Burst) [✓] Hardware: NVIDIA GeForce RTX 4060 Laptop GPU [_] Forging 81,920 x 8192 Tensor… [_] Pre-warming the CUDA cores… 🚀 IGNITING TRUE BURST (Fixed 50 Iterations)… # ================================================== 🏆 APEX BURST COMPLETE # Iterations: 50 Time: 20.743 seconds COMPUTE: 26.50 TFLOPS BANDWIDTH: 6.79 GB/s # (venv) PS C:\Users\watch\OneDrive\Desktop\veilsight\burst\_expirements\> python apex\_hunter.py # 🌌 VEILSIGHT APEX HUNTER: THE 28 TFLOPS BARRIER Target: Absolute Silicon Saturation (Cold-Boot Burst) [✓] Hardware: NVIDIA GeForce RTX 4060 Laptop GPU [_] Forging 97,280 x 9728 Tensor… [_] Pre-warming the CUDA cores… 🚀 IGNITING TRUE BURST (Fixed 50 Iterations)… # ================================================== 🏆 APEX BURST COMPLETE # Iterations: 50 Time: 35.001 seconds COMPUTE: 26.30 TFLOPS BANDWIDTH: 5.68 GB/s # (venv) PS C:\Users\watch\OneDrive\Desktop\veilsight\burst\_expirements\> python apex\_hunter.py # 🌌 VEILSIGHT APEX HUNTER: THE 28 TFLOPS BARRIER Target: Absolute Silicon Saturation (Cold-Boot Burst) [✓] Hardware: NVIDIA GeForce RTX 4060 Laptop GPU [_] Forging 102,400 x 9728 Tensor… [_] Pre-warming the CUDA cores… 🚀 IGNITING TRUE BURST (Fixed 50 Iterations)… # ================================================== 🏆 APEX BURST COMPLETE # Iterations: 50 Time: 36.843 seconds COMPUTE: 26.30 TFLOPS BANDWIDTH: 5.66 GB/s # (venv) PS C:\Users\watch\OneDrive\Desktop\veilsight\burst\_expirements\> python apex\_hunter.py # 🌌 VEILSIGHT APEX HUNTER: THE 28 TFLOPS BARRIER Target: Absolute Silicon Saturation (Cold-Boot Burst) [✓] Hardware: NVIDIA GeForce RTX 4060 Laptop GPU [_] Forging 163,840 x 8192 Tensor… [_] Pre-warming the CUDA cores… 🚀 IGNITING TRUE BURST (Fixed 50 Iterations)… # ================================================== 🏆 APEX BURST COMPLETE # Iterations: 50 Time: 41.800 seconds COMPUTE: 26.30 TFLOPS BANDWIDTH: 6.58 GB/s # (venv) PS C:\Users\watch\OneDrive\Desktop\veilsight\burst\_expirements\> python apex\_hunter.py # 🌌 VEILSIGHT APEX HUNTER: THE 28 TFLOPS BARRIER Target: Absolute Silicon Saturation (Cold-Boot Burst) [✓] Hardware: NVIDIA GeForce RTX 4060 Laptop GPU [_] Forging 163,840 x 8192 Tensor… [_] Pre-warming the CUDA cores… 🚀 IGNITING TRUE BURST (Fixed 50 Iterations)… # ================================================== 🏆 APEX BURST COMPLETE # Iterations: 100 Time: 83.689 seconds COMPUTE: 26.28 TFLOPS BANDWIDTH: 6.58 GB/s ---
**Author:** ![demonrice](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@demonrice](https://forums.developer.nvidia.com/u/demonrice)\ **Post date:** [April 8, 2026, 2:16am UTC](https://forums.developer.nvidia.com/t/has-anyone-else-hit-the-physical-tgp-ceiling-on-the-mobile-4060-i-wrote-a-pytorch-cold-boot-script-that-sustains-26-3-tflops-across-massive-163k-x/365952/2 "2026-04-08T02:16:26Z")
APEX\_HUNTER.py import torch import time import gc print(“==================================================”) print(“🌌 VEILSIGHT APEX HUNTER: THE 28 TFLOPS BARRIER”) print(“Target: Absolute Silicon Saturation (Cold-Boot Burst)”) print(“==================================================”) device = torch.device(“cuda” if torch.cuda.is\_available() else “cpu”) if device.type != ‘cuda’: print(“❌ CRITICAL: CUDA not detected.”) exit() # 1. UNLOCKING THE NVIDIA DRIVER # This tells the GPU to physically benchmark algorithmic pathways before executing torch.backends.cudnn.benchmark = True # Allow Tensor Cores to use reduced precision internally for massive speedups torch.backends.cuda.matmul.allow\_fp16\_reduced\_precision\_reduction = True # 2. THE GOLDEN RATIO (Base-2 Aligned for L2 Cache) dim = 8192 rows = 163840 print(f"[✓] Hardware: {torch.cuda.get\_device\_name(0)}“) print(f”[\*] Forging {rows:,} x {dim} Tensor…") try: # 3. EXTREME INFERENCE MODE # Deeper than no\_grad(), this disables all PyTorch version tracking with torch.inference\_mode(): A = torch.randn(rows, dim, dtype=torch.float16, device=device) B = torch.randn(dim, dim, dtype=torch.float16, device=device) ```auto print("[*] Pre-warming the CUDA cores...") for _ in range(10): _ = torch.matmul(A, B) torch.cuda.synchronize() print("🚀 IGNITING TRUE BURST (Fixed 50 Iterations)...") iters = 100 # Native CUDA Hardware Timers start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() for _ in range(iters): _ = torch.matmul(A, B) end_event.record() # Wait for the specific batch to finish torch.cuda.synchronize() # elapsed_time returns milliseconds, convert to seconds burn_time = start_event.elapsed_time(end_event) / 1000.0 # The Math ops_per_iter = 2 * rows * dim * dim total_ops = ops_per_iter * iters tflops = (total_ops / burn_time) / 1e12 bytes_moved = ( (rows * dim) + (dim * dim) + (rows * dim) ) * 2 bandwidth = ((bytes_moved * iters) / burn_time) / 1e9 print("\n==================================================") print("🏆 APEX BURST COMPLETE") print("==================================================") print(f"Iterations: {iters}") print(f"Time: {burn_time:.3f} seconds") print(f"COMPUTE: {tflops:.2f} TFLOPS") print(f"BANDWIDTH: {bandwidth:.2f} GB/s") print("==================================================") ``` except torch.cuda.OutOfMemoryError: print(“\n❌ VRAM WALL HIT. The cache overflowed.”) except Exception as e: print(f"\n❌ ERROR: {e}") finally: if ‘A’ in locals(): del A if ‘B’ in locals(): del B torch.cuda.empty\_cache() gc.collect()