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.

TorchMorph: CUDA-accelerated Morphological Transforms

arXiv:2608.24738v1 Announce Type: new Abstract: Morphological transforms are long-standing tools for shape and mask processing, but the de facto reference implementation in the Python ecosystem, i.e....

TorchMorph: CUDA-accelerated Morphological Transforms

Morphological transforms are long-standing tools for shape and mask processing, but the de facto reference implementation in the Python ecosystem, i.e. scipy.ndimage, is CPU-only, single-array, and th...

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