TensorFusion.AI
Next-Generation GPU Virtualization and Pooling for Enterprises
Less GPUs, More AI Apps.
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♾️ Tensor Fusion

Tensor Fusion is a state-of-the-art GPU virtualization and pooling solution designed to optimize GPU cluster utilization to its fullest potential.

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🌟 Highlights

📐 Fractional GPU with Single TFlops/MiB Precision

🔄 Battle-tested GPU-over-IP Remote GPU Sharing

⚖️ GPU-first Scheduling and Auto-scaling

📊 Computing Oversubscription and GPU VRAM Expansion

🛫 GPU Live Migration

🎬 Demo

WIP

🚀 Quick Start

Onboard Your Own AI Infra

• Getting Started on Kubernetes
• Getting Started on VM
• Deploy Self-hosted Community Edition

Try it out

• Explore the demo account: Demo Console - Working in progress

• Run following command to try TensorFusion in 3 minutes

# Step 1: Install TensorFusion in Kubernetes
helm install --repo https://nexusgpu.github.io/tensor-fusion/ --create-namespace

# Step 2. Onboard GPU nodes into TensorFusion cluster
kubectl apply -f https://raw.githubusercontent.com/NexusGPU/tensor-fusion/main/manifests/gpu-node.yaml

# Step 3. Check if cluster and pool is ready
kubectl get gpupools -o wide && kubectl get gpunodes -o wide

# Step3. Create an inference app using virtual, remote GPU resources in TensorFusion cluster
kubectl apply -f https://raw.githubusercontent.com/NexusGPU/tensor-fusion/main/manifests/inference-app.yaml

# Then you can forward the port to test inference, or exec shell

(TODO: Asciinema)

💬 Discussion

• Discord channel: https://discord.gg/2bybv9yQNk
• Discuss anything about TensorFusion: Github Discussions
• Contact us with WeCom for Greater China region: 企业微信
• Email us: support@tensor-fusion.com
• Schedule 1:1 meeting with TensorFusion founders

🔮 Features & Roadmap

Core GPU Virtualization Features

• Fractional GPU and flexible oversubscription
• GPU-over-IP, remote GPU sharing with less than 4% performance loss
• GPU VRAM expansion or swap to host RAM
• None NVIDIA GPU/NPU vendor support

Pooling & Scheduling & Management

• GPU/NPU pool management in Kubernetes
• GPU-first resource scheduler based on virtual TFlops/VRAM capacity
• GPU-first auto provisioning and bin-packing
• Seamless onboarding experience for Pytorch, TensorFlow, llama.cpp, vLLM, Tensor-RT, SGlang and all popular AI training/serving frameworks
• Basic management console and dashboards
• Basic autoscaling policies, auto set requests/limits/replicas
• GPU Group scheduling for LLMs
• Support different QoS levels

Enterprise Features

• GPU live-migration, fastest in the world
• Preloading and P2P distribution of container images, AI models, GPU snapshots etc.
• Advanced auto-scaling policies, scale to zero, rebalance of hot GPUs
• Advanced observability features, detailed metrics & tracing/profiling of CUDA calls
• Multi-tenancy billing based on actual usage
• Enterprise level high availability and resilience, support topology aware scheduling, GPU node auto failover etc.
• Enterprise level security, complete on-premise deployment support, encryption in-transit & at-rest
• Enterprise level compliance, SSO/SAML support, advanced audit, ReBAC control, SOC2 and other compliance reports available

🗳️ Platform Support

• Run on Linux Kubernetes clusters
• Run on Linux VMs or Bare Metal (one-click onboarding to Edge K3S)
• Run on Windows (Docs not ready, contact us for support)
• Run on MacOS (Imagining mount a virtual NVIDIA GPU device on MacOS!)

See the open issues for a full list of proposed features (and known issues).

🙏 Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Top contributors

🔷 License

1. This repo is open sourced with Apache 2.0 License, which includes GPU pooling, scheduling, management features, you can use it for free and modify it.
2. GPU virtualization and GPU-over-IP features are also free to use as the part of Community Plan, the implementation is not fully open sourced
3. Features mentioned in "Enterprise Features" above are paid, licensed users can automatically unlock these features.