PyTorch 2.15 Q&A Session on 5 November Release
The default switch to the rewritten NCCL backend and new NIXL one-sided transport affect anyone running distributed training, so tuning assumptions from earlier versions may need revisiting. Engineers building inference on Blackwell or Apple Silicon can check the live overview for concrete decode gains. Registration is open via the official link.
The PyTorch team is hosting a live Q&A on 5 November at 11 AM PT (3 AM SGT, 6 November) about the PyTorch 2.15 release. Speakers include Tristan Rice, Natalia Gimelshein, Huy Do, and Jane Xu from Meta, moderated by Chris Gottbrath of Gottbrath Tech. PyTorch 2.15 makes the rewritten NCCL backend the default and adds one-sided transports with a NIXL backend. It also gives FSDP2 and pipelining finer memory and communication control. The release brings faster NVFP4 4-bit LLM decoding on NVIDIA Blackwell GPUs, FP8 and faster decode on Apple Silicon, and broader platform support.
Bring your questions about PyTorch 2.15 to our live Q&A TOMORROW, November 5, at 11 AM PT.
Tristan Rice (@rice_fry), Natalia Gimelshein, Huy Do, and Jane Xu (@janeyx99) (@Meta), with moderator Chris Gottbrath (Gottbrath Tech), will share an overview of the release and answer community questions about PyTorch and the new capabilities in the release.
PyTorch 2.15 makes the rewritten NCCL backend the default, adds one-sided transports with a NIXL backend, and gives FSDP2 and pipelining finer memory and communication control. The release also brings faster 4-bit (NVFP4) LLM decoding on NVIDIA Blackwell GPUs, FP8 and faster decode on Apple Silicon, and broader platform support.
Register: https://t.co/opFmSkAUNb
