Popular GitHub Repositories for Hardware Requirements

: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

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

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

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

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

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

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

The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curation, in a standardized general environment with minimal requirements.

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.

Hardware for SUSI AI https://susi.ai

AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen? and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency.

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Brevitas: neural network quantization in PyTorch

Stable Diffusion and LLMs offline on your own hardware

Pure python implementation of SNN

A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.