Popular GitHub Repositories for High Throughput Computing

A high-throughput and memory-efficient inference and serving engine for LLMs

Making large AI models cheaper, faster and more accessible

DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.

Composio equip's your AI agents & LLMs with 100+ high-quality integrations via function calling

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

ncnn is a high-performance neural network inference framework optimized for the mobile platform

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

High-performance In-browser LLM Inference Engine

Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang

20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

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

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Refine high-quality datasets and visual AI models

? Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

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

Build, Manage and Deploy AI/ML Systems