Open-source State-of-the-art TTS model which runs on a CPU 😻
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A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
- machine-learning
- decision-trees
- gradient-boosting
- gbm
Open-source library of optimized deep learning operations (matmul, convolution, attention) for CPUs (x64, AArch64, RISC-V) and Intel GPUs. Used by PyTorch, TensorFlow, OpenVINO, and ONNX Runtime.
- onednn
- oneapi
- performance
- cpp
The Swiss Army Knife of Offline AI. Chat, see, speak, and generate images on your phone or Mac — GGUF LLMs, vision, Whisper speech-to-text, Stable Diffusion, tool calling, and local-network servers. Runs on your CPU, GPU, or NPU. No account, no API key, zero data leaves your device.
- privacy-first
- gguf
- llama-cpp
- local-ai
Single C file, Realtime CPU/GPU Profiler with Remote Web Viewer
ILGPU JIT Compiler for high-performance .Net GPU programs
ThunderSVM: A Fast SVM Library on GPUs and CPUs
- classification
- regression
- one-class-learning
- cuda
1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy
CPU and GPU-accelerated Machine Learning Library
eBPF based always-on CPU/GPU profiler auto-discovering targets in Kubernetes and systemd, zero code changes or restarts needed!
斑海豹 — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. 小海豹，大决断。
- bilingual
- calibration
- cpu-inference
- decision-making
Powerful Powerful Machine Learning library with GPU, CPU and WASM backends
A Genetic Programming platform for Python with TensorFlow for wicked-fast CPU and GPU support.
Legend of Elya — N64 game with a real 6.36M-parameter ternary transformer on the VR4300 MIPS III CPU. Zelda-style dungeon, AI NPCs, byte-level inference at 1.23 tok/s scalar / 2.19 tok/s on the RSP overlay (measured under ares, never on silicon). Built with libdragon.
Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.
- autodiff
- automatic-differentiation
- cross-platform
- deep-learning