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Matrix multiplication is a central compute primitive in modern deep learning, but achieving SOTA performance on novel architectures like NVIDIA Blackwell has become a bottleneck. Existing Triton-based kernels in torch.compile struggle to keep pace with rapid hardware evolution, often forcing users to hand-write custom, architecture-specific kernels - a growing gap as hardware feature velocity accelerates.
We present a new CuTeDSL GEMM backend in PyTorch Inductor that integrates NVIDIA’s kernel implementations directly into torch.compile. Built using the Cutlass API for kernel discovery, this backend allows PyTorch to expose first-class support for NVIDIA-authored GEMMs and automatically leverage new architectural features as NVIDIA updates their kernels.
The backend currently supports standard GEMM, grouped GEMM, and block-scaled MXFP8 GEMM, along with pointwise epilogue fusions (with reductions forthcoming). We present early end-to-end results from vLLM inference and TorchTitan training, demonstrating how this approach enables PyTorch to achieve high-performance GEMMs on Blackwell and beyond, while eliminating the need for users or developers to maintain handwritten kernels.
Nikhil is a software engineer on the PyTorch Inductor team at Meta Superintelligence Labs, where he works on Inductor’s CuTeDSL GEMM backend. His work sits at the boundary between compiler code generation and hardware-native GPU features, optimizing large-scale training and inference... Read More →
