tpu
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A high-throughput and memory-efficient inference and serving engine for LLMs
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Feb 20, 2026 - Python
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
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Jun 2, 2023 - Python
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem).
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Feb 20, 2026 - Python
Fast and flexible AutoML with learning guarantees.
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Nov 30, 2023 - Jupyter Notebook
Everything we actually know about the Apple Neural Engine (ANE)
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Oct 21, 2025
GPT2 for Multiple Languages, including pretrained models. GPT2 多语言支持, 15亿参数中文预训练模型
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May 22, 2023 - Python
Large-scale LLM inference engine
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Feb 17, 2026 - C++
Elegant and Performant Deep Learning
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Feb 16, 2026 - Julia
Everything you want to know about Google Cloud TPU
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Jul 16, 2024 - Python
Implementation of a Tensor Processing Unit for embedded systems and the IoT.
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Jan 5, 2019 - VHDL
Differentiable Fluid Dynamics Package
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Jan 29, 2026 - Python
Neural network-based chess engine capable of natural language commentary
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Nov 15, 2022 - C++
JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).
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Jan 5, 2026 - Python
Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot
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Sep 2, 2025
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.
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Dec 2, 2025 - Python
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.
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May 6, 2023 - Shell
Julia on TPUs
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Dec 18, 2020 - Julia
Small-scale Tensor Processing Unit built on an FPGA
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Aug 4, 2019 - Verilog
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.
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Feb 20, 2026 - Python
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