GPU Infra, RedefinedSmart Pooling, Simple Operations
Enterprise-grade GPU pooling and virtualization that reduces costs, boosts elasticity, and simplifies management and operations
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Stats
TensorFusion by the numbers
Enterprise-grade GPU infrastructure trusted by leading organizations
Enterprise Customers
Managed GPUs
Saved costs annually
Core Capabilities
Enterprise-grade GPU infrastructure management
TensorFusion provides comprehensive solutions for GPU pooling, scheduling, and virtualization
Core Capabilities
TensorFusion provides comprehensive solutions for GPU pooling, scheduling, and virtualization
Proven Business Impact
Why leading AI companies choose TensorFusion
Transform your GPU infrastructure with proven technology
Proven Business Impact
Transform your GPU infrastructure with proven technology
- Cut GPU Costs 60-80%
- Reduce Ops Overhead 50%
- Full Stack Observability
- 3x Faster Scaling
Highlights
What makes TensorFusion different
Leverage industry-standard technologies for reliable, scalable infrastructure
Kubernetes Native
Full Kubernetes integration with CRD-based resource management. Seamless integration with existing K8s clusters.
Fractional GPU Sharing
User-space time-divided sharing enables efficient GPU utilization without performance compromise.
Remote GPU Access
API forwarding based GPU-over-IP technology enables remote compute access with minimal latency.
Multi-Vendor Support
Unified interface for heterogeneous GPU vendors. Support for NVIDIA, AMD, and future vendors.
Enterprise Observability
Comprehensive monitoring, metrics, and alerting for heterogeneous AI infrastructure.
Standardized APIs
K8s CRD-based standardized system management APIs for consistent operations across environments.
Ready to transform your GPU cluster and save 60% on costs?
Get started with TensorFusion today and experience the power of software-defined AI computing.
Testimonials
What our customers are saying
Guan Huang
Tensor Engine's VM-based GPU isolation transformed our online AI courses. We cut GPU costs by 80% while serving 200+ concurrent students safely. Each student gets true vGPU isolation—exactly what we needed.
Adam Li
Fractional GPU sharing and intelligent scheduling doubled our utilization. The built-in FinOps module finally solved our cost allocation nightmare across teams.
Sam Li
TensorOS is incredible—full AI stack in one click. We went from bare metal to training models in 3 hours. No DevOps team needed, no cloud lock-in. Perfect for small teams.
Louis Mylle
The heterogeneous GPU pooling is brilliant. We can finally mix NVIDIA and AMD under one scheduler. Saved us months of development work and eliminated vendor lock-in completely.
Wei Wang
Integrating TensorFusion's vGPU engine into our AIOS product took 6 weeks instead of 2 years building from scratch. Production-grade memory isolation right out of the box.
Emily Chen
GPU Go solved our lab's GPU shortage overnight. Mount remote GPUs like network drives—it's that simple. The free tier is perfect for our research group.
Marcus Rodriguez
Went from $800/month cloud bills to $7.9/month with GPU Go. Share my home GPU with my laptop anywhere, borrow from friends' homelabs. Complete game-changer for freelancers.
Priya Sharma
TensorNet lets us pool GPUs across 4 data centers while keeping data local for compliance. Consolidated 200 GPUs down to 120, saving $2.1M annually. Compute flows, data stays.
Michael Torres
We needed AI infrastructure without the complexity. TensorOS gave us everything on our own servers—compliant and secure out of the box. Zero DevOps headcount required.
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