Visualize and understand GPU memory in PyTorch

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Profiling in PyTorch (Part 3): Attention is all you profile

A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting

arXiv:2606. 11390v1 Announce Type: cross Abstract: Gaussian splatting methods have become increasingly popular for neural reconstruction of the real world.

KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?

arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.

OpenAI standardizes on PyTorch

We are standardizing OpenAI’s deep learning framework on PyTorch.

torchsom: The Reference PyTorch Library for Self-Organizing Maps

arXiv:2510. 11147v2 Announce Type: replace-cross Abstract: This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyTorch.

Extensions and limitations of the neural GPU

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

arXiv:2604. 23466v2 Announce Type: replace Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs.

Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

nanoVLM: The simplest repository to train your VLM in pure PyTorch

Accelerating PyTorch distributed fine-tuning with Intel technologies

MegaSlide-DiT: Memory-Centric Adaptation and Deformable Local Attention for Efficient Video Diffusion

arXiv:2607. 22696v1 Announce Type: cross Abstract: High-resolution video diffusion models built on Diffusion Transformers (DiTs) deliver strong fidelity but quickly exhaust the memory budget of a single workstation.