Popular GitHub Repositories for Hardware Impairment

: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.

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

[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

Compare the performance of different LLM that can be deployed locally on consumer hardware. Run yourself with Colab WebUI.

Hardware accelerated, batchable and differentiable optimizers in JAX.

深度学习工程师生存指南