Popular GitHub Repositories for Amd Opencl

A high-throughput and memory-efficient inference and serving engine for LLMs

Open deep learning compiler stack for cpu, gpu and specialized accelerators

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.

QualityScaler - image/video AI upscaler app

dstack is a lightweight, open-source alternative to Kubernetes & Slurm, simplifying AI container orchestration with multi-cloud & on-prem support. It natively supports NVIDIA, AMD, TPU, and Intel accelerators.

NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

SHARK Studio -- Web UI for SHARK+IREE High Performance Machine Learning Distribution

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

Pure Rust implementation of a minimal Generative Pretrained Transformer

Connectionist Temporal Classification (CTC) decoding algorithms: best path, beam search, lexicon search, prefix search, and token passing. Implemented in Python.

Video Frame Interpolation & Super Resolution using NVIDIA's TensorRT & Tencent's NCNN inference, beautifully crafted and packaged into a single app

A ground-up and standalone reimplementation of TensorFlow for ruby. Comes with a pure ruby and OpenCL opcode evaluator

:evergreen_tree: Aimbot powered by real-time object detection with neural networks, GPU accelerated with Nvidia. Optimized for use with CS:GO.

An easy to use C# deep learning library with CUDA/OpenCL support

RealScaler - image/video AI upscaler app (Real-ESRGAN)

Self-host the powerful Chatterbox TTS model. This server offers a user-friendly Web UI, flexible API endpoints (incl. OpenAI compatible), predefined voices, voice cloning, and large audiobook-scale text processing. Runs accelerated on NVIDIA (CUDA), AMD (ROCm), and CPU.

Example code and documentation on how to get Stable Diffusion running with ONNX FP16 models on DirectML. Can run accelerated on all DirectML supported cards including AMD and Intel.

Pure C# machine learning framework