tpu

A high-throughput and memory-efficient inference and serving engine for LLMs

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.

Fast and flexible AutoML with learning guarantees.

Everything we actually know about the Apple Neural Engine (ANE)

Large-scale LLM inference engine

GPT2 for Multiple Languages, including pretrained models. GPT2 多语言支持, 15亿参数中文预训练模型

Machine learning compiler based on MLIR for Sophgo TPU.

Elegant and Performant Deep Learning

Differentiable Fluid Dynamics Package

Implementation of a Tensor Processing Unit for embedded systems and the IoT.

Everything you want to know about Google Cloud TPU

Neural network-based chess engine capable of natural language commentary

OpenVision (ICCV 2025), OpenVision 2 (CVPR 2026), and OpenVision 3

JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).

Frontier-class open models on a free Kaggle TPU v5e-8: GLM-5.3-Flash 320B MoE (~64 tok/s, our own JAX engine) and Qwen3.8-27B bf16 (~130 tok/s), 262k context, prefix caching. Works with Claude Code, Codex, opencode and pi.

DECIMER Image Transformer is a deep-learning-based tool designed for automated recognition of chemical structure images. Leveraging transformer architectures, the model converts chemical images into SMILES strings, enabling the digitization of chemical data from scanned documents, literature, and patents.

Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot

Free TPU for FPGA with compiler supporting Pytorch/Caffe/Darknet/NCNN. An AI processor for using Xilinx FPGA to solve image classification, detection, and segmentation problem.

torchax is a PyTorch frontend for JAX. It gives JAX the ability to author JAX programs using familiar PyTorch syntax. It also provides JAX-Pytorch interoperability, meaning, one can mix JAX & Pytorch syntax together when authoring ML programs, and run it in every hardware JAX can run.