If you have ever installed an NVIDIA professional GPU (Quadro, Tesla, A100, RTX A-series) and opened NVIDIA SMI (System Management Interface) only to see the cryptic flags TCC or WDDM next to your driver type, you have likely asked one question:
| 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 | tcc wddm better
By: Technical Deep Dive Team
For 90% of serious compute workloads—deep learning, AI training, CUDA development, and high-performance computing (HPC)—the answer is a definitive . If you have ever installed an NVIDIA professional
Download NVIDIA CUDA Toolkit (includes nvidia-smi ). Step 2: Open Command Prompt as Administrator. Step 3: Check current mode: Step 3: Check current mode: nvidia-smi -g 0
nvidia-smi -g 0 -dm 1 (0 = WDDM, 1 = TCC)