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github.com
Kernel density estimators for Julia.
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Fast MATLAB-syntax runtime with automatic CPU/GPU execution and fused array kernels.
gpuperhour.com
Real-time GPU cloud price comparison across 30+ providers.
github.com
Integrate LLM technology quickly and easily into your apps.
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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.
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Open Bilingual Pre-Trained Model, quantization of ChatGLM-130B, can run on consumer-level GPUs.
rapids.ai
Open source GPU accelerated data science libraries.
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GPU-based high-performance interactive OpenGL 2D/3D data visualization library.
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A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
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Optimized Stable Diffusion modified to run on lower GPU VRAM.
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CPU and GPU-accelerated Machine Learning Library.
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CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.
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A lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.
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Scalable deep learning for industry with parallel GPUs.
deploybase.ai
Track real-time GPU and LLM pricing across all cloud and inference providers.
github.com
The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.
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Running large language models on a single GPU for throughput-oriented scenarios. (Archived).
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Library for high performance deep learning inference on NVIDIA GPUs.
gestell.ai
Gestell studies PTX, SASS, compiler lowering, and GPU execution behavior.
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Implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
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GPU cluster manager for running and managing LLMs.
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Run Keras models in the browser, with GPU support provided by WebGL 2.
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Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.