Popular GitHub Repositories for Legacy 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 Cloud-Native API Gateway and AI Gateway

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.

A practical architecture styles for migrating from legacy monolith to modern cloud native application with the latest technologies and architectures like Vertical Slice Architecture, Event Sourcing, CQRS, DDD, gRpc, MongoDB, RabbitMq, and Masstransit in .Net 9.

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.