Enable TCC on your compute GPU (e.g., GPU 0):
You can run a single kernel for weeks without interruption. Furthermore, TCC allows for "Peer-to-Peer" (P2P) transfers between GPUs (NVLink) without copying memory through system RAM. WDDM often blocks direct P2P for stability reasons. 3. Remote Desktop (RDP) Support This is the "killer feature" for data scientists. With a WDDM GPU connected to a headless server (no monitor), Windows Remote Desktop will not render CUDA properly. You usually get errors like "CUDA driver version insufficient for runtime version." tcc wddm better
For 90% of serious compute workloads—deep learning, AI training, CUDA development, and high-performance computing (HPC)—the answer is a definitive . Enable TCC on your compute GPU (e
| Test | WDDM Mode (Standard) | TCC Mode | Improvement | | :--- | :--- | :--- | :--- | | | 3,450 | 4,120 | +19.4% | | CUDA Memcpy (Host to Device) | 12.4 GB/s | 25.1 GB/s | +102% (Bypasses PCIe limits imposed by WDDM) | | Kernel Launch Overhead (100k launches) | 2.4 seconds | 0.9 seconds | -62% | | Multi-GPU Scaling (2x GPUs) | 1.6x speedup | 1.95x speedup | Near-native NVLink speed | You usually get errors like "CUDA driver version
Reboot the machine.
Download NVIDIA CUDA Toolkit (includes nvidia-smi ). Step 2: Open Command Prompt as Administrator. Step 3: Check current mode:
