TensorFusion DocsOverview
Compare TensorFusion with other open source or commercial solutions
Overview
TensorFusion is a cutting-edge GPU virtualization and pooling platform that offers comprehensive AI infrastructure management. This section provides detailed comparisons with other GPU virtualization and pooling solutions to help you understand TensorFusion's unique advantages.
Why Compare?
Choosing the right GPU virtualization solution is crucial for your AI infrastructure. Different solutions offer varying levels of:
- Feature completeness - From basic GPU sharing to advanced enterprise features
- Performance impact - How much overhead the virtualization layer introduces
- Ease of deployment - Complexity of setup and ongoing maintenance
- Cost effectiveness - Total cost of ownership including licensing and operational costs
- Vendor lock-in - Open source vs proprietary solutions
Comparison Summary
| Feature | TensorFusion | HAMi | NVIDIA vGPU | Run.AI | VirtAI OrionX | MIG/MPS |
|---|---|---|---|---|---|---|
| Fractional GPU | ||||||
| GPU Pooling | ||||||
| Remote GPU Sharing | ||||||
| GPU Resource Oversubscription | ||||||
| GPU VRAM Expansion | ||||||
| GPU Live Migration | ||||||
| Open Source |
TensorFusion
Complete GPU virtualization platform with remote sharing, pooling, and enterprise features. Open source with commercial support.
HAMi
Popular open source GPU pool management with fractional GPU and dynamic MIG features for multi-vendor GPUs.
NVIDIA vGPU
NVIDIA's proprietary GPU virtualization solution with hefty license fees. Limited to single GPU virtualization.
Run.AI
Closed source GPU management platform (acquired by NVIDIA). Offers pooling and fractional GPU capabilities.
VirtAI OrionX
Closed-source GPU pooling solution with enterprise features. Similar to HAMi but more powerful and enterprise-ready.
MIG/MPS
NVIDIA's official GPU partitioning and sharing method. Limited to 7 sub-GPUs and lacks advanced features.
Detailed Comparisons
Explore comprehensive comparisons with each solution to understand their strengths, weaknesses, and total cost of ownership:
vs HAMi
Compare TensorFusion with the popular open source GPU pool management solution.
Read Comparison →
vs NVIDIA vGPU
See how TensorFusion compares to NVIDIA's proprietary virtualization solution.
Read Comparison →
vs VirtAI OrionX
Compare with the enterprise-ready closed source GPU pooling solution.
Read Comparison →
Key Differentiators
TensorFusion is the only solution that delivers true GPU virtualization, remote sharing, and comprehensive AI infrastructure management in a single platform.
Unique TensorFusion Advantages
- True GPU Virtualization: Complete virtual memory address space, error isolation, and resource oversubscription
- Zero-intrusion Remote Sharing: GPU-over-IP with less than 5% performance loss
- GPU Memory Tiering: Hot/warm/cold tiering with second-level swapping between GPU and host memory
- Automated Management: Full-fledged GPU/NPU pool management with monitoring, alerting, and bin-packing
- Open Source: Core components are open source while maintaining enterprise-grade features
- Vendor Agnostic: Designed to work with multiple GPU vendors, not just NVIDIA
Pricing Advantage
- Free for Small Teams: Up to 800 FP16 TFLOPs (equivalent to 12 NVIDIA T4 GPUs)
- Cost Effective: Less than 4% of computing cost for larger deployments
- High ROI: Achieve 50%+ cost savings with 2500%+ ROI
- No Vendor Lock-in: Open source core with transparent pricing
Getting Started
Ready to experience TensorFusion's advantages? Get started with our quick deployment guides: