TL;DR TorchTPU is an open source collaboration between Google and Meta that introduces a native and principled PyTorch backend for Google TPUs. It enables seamless out-of-the-box model execution and TPU...
TL;DR Torch-spyre’s Inductor decides how each kernel’s work is divided across Spyre’s cores and manages tensor residency between on-chip scratchpad and off-chip memory, and the backend compiler turns those decisions...
Frontier models span language, video and audio, on architectures from dense and mixture-of-experts transformers to diffusion, state-space, world and structure-prediction models. PyTorch is where AI research happens, where the models...
Why Zephon? The data that a model sees during training is one of the most powerful levers we have for influencing how well it performs. Much of the day-to-day work...
TL;DR Deploying PyTorch models on microcontrollers has historically required converting to other framework formats, maintaining separate toolchains, and debugging cross-framework numerical mismatches. ExecuTorch eliminates this conversion step entirely. The Cortex-M...
TL;DR The PyTorch-based software stack for IBM’s Spyre accelerator recently moved its torch.compile backend from sendnn, a separate compilation stack built entirely from scratch, to backend=inductor, an open-source backend built...
TL;DR Agentic workloads change the traffic an inference server sees. Unlike single-turn chat, an agent session can involve a large initial prefill, repeated model calls, and subagents running in parallel,...
TL;DR Spyre becomes a native PyTorch device by connecting PyTorch’s existing device, allocator, stream, and compiler abstractions through torch-spyre to the Spyre runtime and firmware. PrivateUse1 gives Spyre a real...
This post explores the FBTriton kernel design for Table Batched Embedding (TBE) forward and backward passes. These core operators handle embedding lookups across thousands of sharded GPUs within recommendation systems....
TL;DR If you need to decode or encode media, whether it’s images, video, or audio, use TorchCodec. If you need to transform media, use TorchVision for images and video, and...
TL;DR The Accelerator Integration Working Group plays a vital role in standardizing how new hardware architectures connect to the open source AI ecosystem. As compute platforms diversify across cloud, edge,...
TL;DR In this blog post, we present our work on Jagged Flash Attention (JFA) — the attention kernel behind Meta’s Generative Ads Model (GEM) — on NVIDIA Blackwell (B200), built...
TL;DR PyTorch’s Cross-Repository CI Relay (CRCR) gives out-of-tree accelerators a clean, scalable way to plug into upstream CI – and it leaves each backend free to decide which of PyTorch’s...
As deep learning models move rapidly from research prototypes into core enterprise infrastructure, the demand for practical, end-to-end PyTorch expertise has never been higher. Building robust neural networks requires more...
TL;DR: This case study explores how Shopify compresses production failures into model weights every day, beats frontier-model quality, and cuts serving costs 96% by building a continual learning loop with...
A framework you write yourself, tensors through transformers TL;DR Every mature systems project eventually needs a teaching version. TinyTorch is a free, open-source curriculum where you build a working ML...
TL;DR Taking place October 20 to 21 in San Jose, California, PyTorch Conference North America 2026 highlights open research, tooling, and performance optimization across compiler architecture, cross-hardware kernel domain-specific languages,...
TL;DR The HuggingFace Kernels project now has Helion support. This blog walks through how to build, autotune, and ship performant and portable Helion kernels via the Hugging Face Kernels project,...
TL;DR More than 170 students, engineers, researchers, and open-source contributors gathered in Bengaluru for a technical evening hosted by Red Hat and Hugging Face around PyTorch, large-scale inference, reinforcement learning...
TL;DR PyTorch Conference North America 2026 features Agentic AI and Next-Gen Intelligence across sessions on training agents, serving agents in production, agents that build PyTorch, and PyTorch in the physical...
