Popular GitHub Repositories for Hardware Aware

:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P inference

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Context aware, pluggable and customizable data protection and de-identification SDK for text and images

Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

A Streamlit-powered Chat Assistant utilizing Ollama's DeepSeek-R1 (1.5B) model for intelligent, context-aware conversations. Designed with an interactive chat interface, adjustable model parameters. It leverages Ollama for local model serving and Streamlit for a responsive UI for text-based interactions.

Sparsity-aware deep learning inference runtime for CPUs

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

? Accelerate inference and training of ? Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools

A Curated list of IoT Security Resources

Massively parallel rigidbody physics simulation on accelerator hardware.

Open source neural network chess engine with GPU acceleration and broad hardware support.

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet

Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

? Monitor deep learning model training and hardware usage from your mobile phone ?

Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.

This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture.

GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]