Popular GitHub Repositories for Gpus

Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

NVIDIA? TensorRT? is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way.

An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries

Postgres with GPUs for ML/AI apps.

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

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. ?? Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs.

[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.

Best inference performance optimization framework for HuggingFace Diffusers on NVIDIA GPUs.

Spark RAPIDS plugin - accelerate Apache Spark with GPUs

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

List of Deep Learning Cloud Providers

Intel? AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel? Xeon? Scalable processors and Intel? Data Center GPUs

World's fastest ANPR / ALPR implementation for CPUs, GPUs, VPUs and NPUs using deep learning (Tensorflow, Tensorflow lite, TensorRT, OpenVX, OpenVINO). Multi-Charset (Latin, Korean, Chinese) & Multi-OS (Jetson, Android, Raspberry Pi, Linux, Windows) & Multi-Arch (ARM, x86).

Automatically split your PyTorch models on multiple GPUs for training & inference

GPU environment and cluster management with LLM support

Run LLaMA (and Stanford-Alpaca) inference on Apple Silicon GPUs.

Felafax is building AI infra for non-NVIDIA GPUs

Multi-threaded GUI manager for mass creation of AI-generated art with support for multiple GPUs.