MatrixHub

MatrixHub is an open-source, self-hosted AI model registry engineered for large-scale enterprise inference. It serves as a drop-in private replacement for Hugging Face, purpose-built to accelerate vLLM and SGLang workloads.

💡 Why MatrixHub?

MatrixHub streamlines the transition from public model hubs to production-grade infrastructure:

• Zero-Wait Distribution: Eliminate bandwidth bottlenecks with a "Pull-once, serve-all" cache, enabling 10Gbps+ speeds across 100+ GPU nodes simultaneously.
• Air-Gapped Delivery: Securely ferry models into isolated networks while maintaining a native HF_ENDPOINT experience for researchers—no internet required.
• Private AI model Registry: Centralize fine-tuned weights with Tag locking and CI/CD integration to guarantee absolute consistency from development to production.
• Global Multi-Region Sync: Automate asynchronous, resumable replication between data centers for high availability and low-latency local access.

🛠️ Core Features

🚀 High-Performance Distribution

• Transparent HF Proxy: Switch to private hosting with zero code changes by simply redirecting your endpoint.
• On-Demand Caching: Automatically localizes public models upon the first request to slash redundant traffic.
• Inference Native: Native support for P2P distribution, OCI artifacts, and NetLoader for direct-to-GPU weight streaming.

🛡️ Enterprise Governance & Security

• RBAC & Multi-Tenancy: Project-based isolation with granular permissions and seamless LDAP/SSO integration.
• Audit & Compliance: Full traceability with comprehensive logs for every upload, download, and configuration change.
• Integrity Protection: Built-in malware scanning and content signing to ensure models remain untampered.

🌍 Scalable Infrastructure

• Storage Agnostic: Compatible with local file systems, NFS, and S3-compatible backends (MinIO, AWS, etc.).
• Reliable Replication: Policy-driven, chunked transfers ensure data consistency even over unstable global networks.
• Cloud-Native Design: Optimized for Kubernetes with official Helm charts and horizontal scaling capabilities.

🚀 Quick Start

Docker Compose Deployment

Use Docker Compose with the provided configuration files:

• website/static/deploy/docker/docker-compose.yaml
• website/static/deploy/docker/config.yaml

Make sure docker-compose.yaml and config.yaml are in the same folder, then start the service:

docker compose -f docker-compose.yaml up -d

Default service endpoint:

http://127.0.0.1:3001

Helm (Kubernetes) Deployment

Install MatrixHub using the built-in Helm chart:

helm install matrixhub ./deploy/charts/matrixhub

Expose it locally (default ClusterIP) via port-forward:

kubectl port-forward deploy/matrixhub-apiserver 9527:9527

Or expose it via NodePort:

helm install matrixhub ./deploy/charts/matrixhub --set apiserver.service.type=NodePort

📚 Docs

• Documentation site
• Development guide

Community, discussion, contribution, and support

Slack is our primary channel for community discussion, contribution coordination, and support. You can reach the maintainers and community at:

• Slack