Video by PyTorch via YouTube
PyTorch 2.14 introduces updates across compilation, distributed training, performance, dynamic shapes, Apple Silicon, and accelerator platforms. Highlights include NVGEMM, a CuTeDSL-generated GEMM backend for Inductor; the new nccl2 backend for PyTorch Distributed; fault-tolerant process-group reconfiguration in c10d; native linear algebra on Apple Silicon; and declarative dynamic shapes with @dynamic_spec.
Bring your questions about the release to our live Q&A. Andrey Talman (Meta), Natalia Gimelshein (Meta), Joe Spisak (Reflection AI), and Chris Gottbrath (Gottbrath Tech, moderator) will share an overview of PyTorch 2.14 and answer community questions about PyTorch and the new capabilities in the release.
Topics will include:
– NVGEMM and CuTeDSL-generated CUTLASS kernels in Inductor
– The new nccl2 backend for PyTorch Distributed
– Fault-tolerant collectives and process-group reconfiguration in c10d
– Native linear algebra and additional Metal kernel improvements on Apple Silicon
– torch.switch and CUDA graph capture for torch.while_loop
– Declarative dynamic shapes with @dynamic_spec
– Experimental torch.compile support for complex-valued tensors
– Expanded ROCm, Intel XPU, and NVIDIA platform support
PyTorch 2.14 includes 2,995 commits from 487 contributors since PyTorch 2.13. The release includes work across compilation, distributed communication, device support, and accelerator platforms. PyTorch Conference North America 2026 takes place October 20–21 in San Jose, with sessions spanning compiler and runtime work, distributed communication, device portability, release engineering, CI, observability, accelerator integration, contributor infrastructure, and more. Explore PyTorch Conference North America 2026.