Andrew Kerr

🎉CUTLASS 4.0 is here-bringing native #Python support for device-side kernel design, for ops like GEMM, Flash Attention, and more, powered by the new CuTe DSL. For the first time, you can write high-performance GPU kernels in Python with the same abstractions, APIs, and performance as CUTLASS C++-no compromises. The learning curve for writing optimized kernels is flattened: no more wrestling with C++ templates or long compile times. CUTLASS 4.0’s Python support delivers: 👀 🏎️ Performance on par with C++ kernels ⏱️ 100x+ faster compile times 🤔 Intuitive, Python-native syntax ⚒️ No need for NVCC installs-just pip install nvidia-cutlas-dsl and go 🤝 Seamless integration with PyTorch and the broader Python ecosystem 📚 Improved documentation and a better debugging experience: docs.nvidia.com/cutlass/ Key features in #CUTLASS 4.0: ✅ CuTe DSL: Python-native, low-level programming model mirroring CuTe C++ abstractions (layouts, tensors, thread/data hierarchy) ✅ Supports for NVIDIA Ampere, Ada, Hopper, and Blackwell Tensor Cores ✅ Examples and Jupyter notebooks for rapid onboarding ✅ Further improved Blockwise and Groupwise GEMMs on Hopper and Blackwell Whether you’re a researcher, student, or ML engineer, CUTLASS 4.0 with Python lowers the barrier to high-performance GPU programming and accelerates the path from prototype to production. 📝 Examples: github.com/NVIDIA/cutlass/tr… 📗 Jupyter notebooks: github.com/NVIDIA/cutlass/tr… We’re excited to see what you build-feedback and contributions welcome. 🙌 (Note: CuTe DSL is currently in public beta and will evolve with community feedback. C++ APIs remain fully supported for existing workflows).