The Forge Cross-Platform Framework PC Windows, Steamdeck (native), Ray Tracing, macOS / iOS, Android, XBOX, PS4, PS5, Switch, Quest 2

Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP

Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.

Multi-threaded GUI manager for mass creation of AI-generated art with support for multiple GPUs.

Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs

Efficient Distributed GPU Programming for Exascale, an SC/ISC Tutorial

Face recognition system for ID photos

GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.

QUDA is a library for performing calculations in lattice QCD on GPUs.

Auto-tuned launcher for GGUF models on llama.cpp / ik_llama.cpp — OpenAI-compatible server with multi-GPU tensor-split, MoE expert placement, measured flag tuning (AI Tune), hardware-matched HuggingFace downloads, and crash recovery. An Ollama alternative for multi-GPU rigs.

A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.

Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python

TorchDR - PyTorch Dimensionality Reduction

Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid

Code for training py-faster-rcnn and py-R-FCN on multiple GPUs in caffe

The world's first CUDA implementation of Weakly-Compressible Smoothed Particle Hydrodynamics

Chains stable-diffusion-webui instances together to facilitate faster image generation.

Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.

multi-gpu pre-training in one machine for BERT without horovod (Data Parallelism)

High-level C++ for Accelerator Clusters