The PyTorch Docathon 2026, held from May 5 to May 19, resulted in over 150 merged pull requests after more than 260 registrants and 30 active participants contributed fixes, API documentation, and Exe…
PyTorch 2.11 now enables direct installation of CUDA-enabled PyTorch wheels on aarch64 Linux from PyPI, eliminating the need for custom package indexes and workarounds that previously complicated depl…
The ExecuTorch MLX delegate now enables GPU-accelerated inference for PyTorch models on Apple Silicon Macs through Apple's MLX framework. The new backend achieves 3-6x higher throughput on generative …
PyTorch 2.12 introduces a new device-agnostic `torch.accelerator.Graph` API that unifies graph capture and replay across CUDA, XPU, and out-of-tree backends. The release delivers up to 100x faster bat…
Arm has released a set of hands-on Jupyter labs demonstrating how to deploy AI models on edge devices using ExecuTorch, an extension of the PyTorch ecosystem designed for local inference on constraine…
Meta researchers developed In-Kernel Broadcast Optimization (IKBO), a kernel-model-system co-design that eliminates redundant user-embedding replication during recommendation model inference. Deployed…
Shepherd Model Gateway (SMG) has disaggregated all CPU-bound workloads from GPU inference in large language model serving, moving tokenization, detokenization, and parsing into a dedicated Rust gatewa…
Researchers at Microsoft have introduced AutoSP, a compiler-based solution that automatically converts standard training code into multi-GPU sequence parallel code for long-context language model trai…