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Explore the Linux kernel source code with AI-generated summaries.

Kernel density estimators for Julia.

Fast MATLAB-syntax runtime with automatic CPU/GPU execution and fused array kernels.

Real-time GPU cloud price comparison across 30+ providers.

Integrate LLM technology quickly and easily into your apps.

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

Open Bilingual Pre-Trained Model, quantization of ChatGLM-130B, can run on consumer-level GPUs.

Open source GPU accelerated data science libraries.

GPU-based high-performance interactive OpenGL 2D/3D data visualization library.

A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.

Optimized Stable Diffusion modified to run on lower GPU VRAM.

CPU and GPU-accelerated Machine Learning Library.

CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.

A lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

Scalable deep learning for industry with parallel GPUs.

Track real-time GPU and LLM pricing across all cloud and inference providers.

The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.

Running large language models on a single GPU for throughput-oriented scenarios. (Archived).

Library for high performance deep learning inference on NVIDIA GPUs.

Gestell studies PTX, SASS, compiler lowering, and GPU execution behavior.

Implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

GPU cluster manager for running and managing LLMs.

Run Keras models in the browser, with GPU support provided by WebGL 2.

Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.