AI Infrastructure

Apache Mahout develops Qumat, a backend agnostic quantum circuit library, and QDP, a GPU accelerated quantum data encoding engine.

Quix is the agentic AI platform for hardware engineering — turn test-rig and sensor data into real-time insight.

OptiMate is a collection of libraries for optimizing AI model performance; the repository is marked as a legacy project.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

9Router routes AI requests across providers with automatic fallback and OpenAI-compatible endpoints.

Kedro-Viz visualizes Kedro data and machine learning pipelines and tracks experiments.

MLflow helps teams build AI products by debugging, evaluating, monitoring, and improving AI applications.

BaseAI helps developers build serverless AI agents with memory, tools, and composable workflows for local development and deployment.

FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.

Burn provides a flexible, efficient, and portable tensor library for building deep learning applications.

Apache SINGA supports distributed deep learning through a system and library for model training.

Pachyderm provides data-focused pipelines and version control for datasets and related workflows.

T-Rex Label uses visual models for image annotation, including bounding boxes and segmentation, and supports COCO and YOLO formats.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

Flexible, high-performance serving system for machine learning models.

Harbor sets up a preconfigured LLM stack with one command and includes hundreds of services to explore.

Mirascope describes itself as an alternative approach to building applications with large language models.

BurnRate tracks AI model provider spending in a dashboard and offers a local CLI for agent-level costs.

Pathway offers cloud templates for RAG, AI pipelines, and enterprise search that stay synchronized with connected data sources.

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.

ModelFox supports training, deployment, and monitoring for machine learning models.

AssemblyAI offers speech models for transcription and extracting information from voice data.

Cascade supports MLOps workflows for smaller projects without a full platform.

Nos dynamically partitions GPU resources and manages quotas in Kubernetes clusters.