multi-gpu

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

Open-source AI computer builds, from 2-GPU desktops to 8-GPU servers: CAD, BOM, BIOS and build guides. Get the case kit with PCIe risers, or ask us for a free fit check.

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

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

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

llama.cpp/ik_llama.cpp launcher: loads big MoE models across mismatched multi-GPU rigs by exact VRAM math.

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

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.

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.

SSD-streaming inference engine for giant MoE models (Rust + CUDA). GLM 5.2 743B at 2 tok/s and Hy3 295B at 7 tok/s on two consumer 16GB GPUs. Zero-config multi-GPU: measures PCIe bandwidth, places attention and hot experts where they fit.

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.