AI Infra Partners: Building a Federated Compute Network with SLA Control

"We had GPUs—just not in the right place at the right time"

An infrastructure partner operates over 500 GPU cards across 6 regions and 12 data centers. On paper, total capacity looks sufficient; but in reality, delivery feels like "a bit everywhere, but never enough":

• East China Cluster: GPUs fully loaded during peak hours, tasks queued for 2-4 hours, constant customer complaints
• South China Cluster: GPU utilization consistently below 35%, significant idle resources
• North China Cluster: GPUs available, but unusable because customer data cannot cross regions
• Southwest Cluster: Inference task latency fluctuates wildly, SLA breach rate as high as 5%

Enterprise customers aren't just asking for "more GPUs." They're asking for one contract-level promise: controllable SLAs, unified operations, and someone to backstop when things go wrong.

Three Core Pain Points: Data Compliance, Resource Fragmentation, and Uncontrollable SLAs

Pain Point 1: Data Locality is a Hard Rule, Not a Suggestion

In regulated industries like finance, healthcare, and government, "just move the data" often doesn't work:

• Compliance Requirements: Data must be strictly confined to specified regions/jurisdictions. Cross-region transmission violates the Data Security Law and Personal Information Protection Law
• Customer Trust: Enterprise customers demand data sovereignty—data cannot leave local data centers
• Traditional Solution Limitations: Can only deploy GPUs independently by region, unable to share across regions, leading to resource waste

Real Case: A financial customer required data to remain in Beijing, but the Beijing cluster was GPU-saturated while the Shanghai cluster had idle resources that couldn't be used. The customer was eventually lost.

Pain Point 2: Resource Fragmentation Leads to Low Utilization and High Costs

Multi-cluster, multi-region GPU resources create an "island effect":

• Uneven Resource Distribution: Some clusters have GPUs idle for extended periods (utilization only 30-40%), while others are GPU-saturated during peak hours with task queues
• No Elastic Scheduling: Even if adjacent clusters have idle GPUs, they cannot be used by other clusters—severe resource fragmentation
• Cost Waste: Each cluster must be configured for peak demand, but actual average utilization is only 40-50%, with significant idle resources

Quantified Impact:

• Overall GPU utilization: 42% (industry average)
• Cross-cluster resource utilization: 0% (completely fragmented)
• Annual GPU cost waste: approximately 35-40% (configured for peak but low average utilization)

Pain Point 3: SLAs Hard to Guarantee, Enterprise Customer Churn

Enterprise customers need SLA commitments that can be written into contracts, but traditional solutions struggle to deliver:

• Uncontrollable Latency: Cross-cluster task latency fluctuates widely, P95 latency ranging from 200ms to 2 seconds
• Availability Hard to Guarantee: Cannot automatically failover when a single cluster fails, SLA breach rate as high as 3-4%
• Priority Chaos: Inference tasks and batch processing tasks mixed together, critical tasks cannot be guaranteed

Business Impact:

• Enterprise customer churn rate: approximately 25% (due to inability to guarantee SLAs)
• SLA breach rate: 3-4% (exceeding the 1% specified in contracts)
• Cross-region task success rate: approximately 90% (below the 99% required by enterprise customers)

How TensorFusion Perfectly Solves the Three Pain Points

TensorFusion enables data compliance, resource pooling, and SLA guarantees to coexist through GPU-over-IP technology, federated scheduling, and policy-based SLA management.

Core Technology: Data Stays Put, Compute Moves (Compute-to-Data)

TensorFusion's core innovation is GPU-over-IP technology, achieving true "data stays put, compute moves":

1. GPU Remote Sharing: Share GPU compute power remotely over IP networks (InfiniBand supported), with data always remaining local and compute power scheduled over the network to where data resides
2. Less than 5% Performance Loss: Deeply optimized GPU-over-IP technology keeps performance loss under 5%, fully meeting real-time inference latency requirements
3. Zero-Intrusion Deployment: Based on Kubernetes native extensions, no need to modify existing application code, just add annotations to integrate

Why This Solves Data Compliance Issues?

• Data always remains in local clusters, never cross-region transmission
• Only GPU compute power flows over the network, data stays completely static
• Meets compliance requirements such as the Data Security Law and Personal Information Protection Law
• Through policy configuration, can hard-limit data from crossing specific boundaries

Solution 1: Cross-Cluster Federated Scheduling, Breaking Resource Islands

TensorFusion's federated scheduler makes intelligent decisions based on real-time signals:

• Real-Time Capacity Awareness: Real-time monitoring of GPU available capacity, health status, and saturation across clusters
• Intelligent Task Placement: Comprehensively considers distance, network conditions, and load balancing to automatically place tasks in optimal clusters
• Resource Pooling: Unifies fragmented GPU resources into a federated resource pool, achieving cross-cluster compute sharing

Technical Advantages:

• Scheduling not based on static tables, but dynamic decisions from real-time signals
• Supports multi-level scheduling strategies: cluster-level, node-level, GPU-level
• Automatic Failover: Automatically switches to other clusters when a single cluster fails

Why This Improves Utilization?

• Breaks resource islands, achieving cross-cluster resource sharing
• Intelligent scheduling ensures resources are allocated where most needed
• Supports GPU virtualization and oversubscription, further improving utilization

Solution 2: Policy-Based Boundary Management (Compute-to-Data)

TensorFusion turns "cannot cross" boundaries into executable rules through policy configuration:

• Region/Jurisdiction Restrictions: Through policy configuration, hard-limit specific tasks to execute only in specified regions
• Tenant Isolation Requirements: Tasks from different tenants are completely isolated, non-interfering
• Dataset Residency Policies: Tasks for specific datasets can only use local or specified cluster GPUs

Technical Implementation:

• Policy configuration based on Kubernetes CRD, rules are programmable and auditable
• Policy engine automatically executes during scheduling, no manual intervention needed
• Supports complex multi-dimensional policy combinations (region + tenant + dataset)

Why This Ensures Data Compliance?

• Policy as code, rules are auditable and traceable
• Scheduler automatically checks policies when placing tasks, directly rejects violating tasks
• No need to rely on human memory and checks, reducing human error

Solution 3: SLA-Aware Task Placement and Priority Guarantees

TensorFusion supports fine-grained SLA management and priority scheduling:

• Inference Tasks Priority: Latency-sensitive inference services get priority placement and reserved headroom
• Batch Tasks Absorb Remaining Capacity: Batch and offline tasks automatically absorb remaining capacity without affecting critical tasks
• SLA Monitoring and Alerting: Real-time monitoring of SLA metrics, automatic alerting and scheduling adjustments when thresholds are exceeded

Technical Features:

• Supports multi-level QoS: critical tasks, normal tasks, low-priority tasks
• Reserved Resource Pool: Dedicated resources reserved for critical tasks to ensure SLAs
• Auto Scaling: Automatically adjusts resource allocation based on SLA requirements

Why This Guarantees SLAs?

• Priority scheduling ensures critical tasks always get sufficient resources
• Reserved resource pools avoid latency fluctuations from resource competition
• Real-time monitoring and automatic adjustments significantly reduce SLA breach rates

Real Data Comparison: Metrics Before and After Optimization

Based on actual customer cases, improvements brought by TensorFusion are as follows:

Core Metrics Comparison

Business Metrics Comparison

Technical Metrics Comparison

Why TensorFusion Can Perfectly Solve These Problems?

1. The Only GPU Virtualization Solution Supporting "Data Stays Put, Compute Moves"

TensorFusion is the industry's only GPU virtualization solution that simultaneously achieves:

• True GPU Virtualization: Achieves GPU virtual addressing, error isolation, and resource oversubscription
• GPU-over-IP Remote Sharing: Less than 5% performance loss, zero intrusion to business
• Policy-Based Boundary Management: Hard-limits data from crossing specific boundaries through programmable policies

Comparison with Other Solutions:

• NVIDIA vGPU: Does not support GPU-over-IP, cannot share across clusters
• Run.AI: Does not support GPU-over-IP, does not support true compute slicing and scheduling, does not support policy-based boundary management
• HAMi: Open source but limited functionality, does not support GPU-over-IP, does not support federated scheduling

2. Kubernetes Native, Progressive Integration

• Zero-Intrusion Deployment: Based on Kubernetes extensions, no need to modify existing applications
• Progressive Integration: Can gradually connect clusters to the federated network without affecting existing business
• Unified Management: Unified management of all clusters through TensorFusion console

3. Clear Cost Advantages

• Community Edition Free: Completely free for GPU compute power not exceeding 800 FP16 TFLOPs (equivalent to 12 T4 GPUs)
• Low Commercial Pricing: Charges only less than 4% of compute cost as subscription price, far lower than vGPU, Run.AI, and other solutions
• High ROI: Through resource pooling and utilization improvement, achieves 50%+ cost savings with ROI exceeding 2500%

Why This Becomes a Business Advantage

Federation is not just "technical plumbing"—it's a commercial lever. TensorFusion turns fragmented GPU inventory into a controllable, operable, scalable SLA-guaranteed compute network—this often directly determines whether you can win larger enterprise deals.

Business Value

1. Increase Customer Signing Rate: After being able to guarantee SLAs, enterprise customer signing rate increased by 40%
2. Reduce Customer Churn Rate: SLA breach rate dropped from 3-4% to <1%, customer churn rate reduced by 80%
3. Cost Optimization: GPU utilization increased by 60-100%, annual costs saved by 30-40%
4. Ops Simplification: Unified management reduced ops costs by 30-40%

Competitive Advantages

• Technical Leadership: Industry's only GPU virtualization solution supporting "data stays put, compute moves"
• Cost Advantage: Pricing far lower than closed-source commercial solutions, ROI exceeding 2500%
• Heterogeneous Compute: Supports heterogeneous compute scheduling, supporting mainstream domestic GPUs as well as AMD and NVIDIA

TensorFusion, through technological innovation, achieves cross-cluster compute resource sharing while meeting data compliance requirements, ensuring both data sovereignty and improved resource utilization, while significantly reducing costs. It is the ideal solution for AI infrastructure partners building federated compute networks.