| Operation | PyTorch 2.4 + CUDA 12.4 | PyTorch 2.6 + CUDA 12.6 | Improvement | |-----------|------------------------|-------------------------|-------------| | MFU (Model FLOPs utilization) | 38.2% | 40.5% | +2.3% | | Kernel launch time (microbench) | 12.4 µs | 8.2 µs | -34% | | cuDNN attention forward (512 seq len) | 0.43 ms | 0.39 ms | -9% |
: The torch.cuda.MemPool() API has reached stability, allowing developers to mix multiple CUDA system allocators within a single program—highly useful for 12.6-optimized workloads. 🛠️ Key Compatibility Facts pytorch cuda 12.6 news
: While CUDA 13.0 is now the default for pip install torch on PyPI, CUDA 12.6 is the recommended fallback for users with Maxwell (SM 5.0) and Pascal (SM 6.0) GPUs, which are no longer supported by the CUDA 13.x toolkits. | Operation | PyTorch 2
Let me know if you want me to add anything else! The PyTorch team has recently announced support for CUDA 12
The PyTorch team has recently announced support for CUDA 12.6, the latest version of NVIDIA's popular parallel computing platform. This update brings several performance improvements, new features, and bug fixes to PyTorch users.
: Standard PyTorch binaries ship with their own CUDA runtime. You do not need to install the full CUDA 12.6 Toolkit unless you are building custom C++ extensions.