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 operated GPUs across multiple regions and data centers. On paper, supply looked healthy. In reality, it was fragmented:

one cluster had idle capacity
another had a backlog
a third couldn’t be used because data couldn’t move

Enterprise customers weren’t asking for “more GPUs.” They were asking for one contract-level promise: predictable SLAs with unified operations.

“When we couldn’t guarantee placement and latency, deals stalled—even though we had capacity.” — Partner Ecosystem Lead

The core constraint: data locality is not negotiable

In regulated industries and sensitive workloads, “just move the data” is often impossible. So the only scalable strategy is the reverse:

keep data where it is, and move compute to it.

What changed with TensorFusion

1) Federated scheduling across clusters

Jobs were placed based on real-time signals:

available GPU capacity
health + saturation
proximity and network conditions

2) Compute-to-data routing by policy

Policies encoded boundaries:

region / jurisdiction rules
customer tenancy rules
dataset residency constraints

3) SLA-aware placement for inference

Latency-sensitive inference got priority placement and reserved headroom, while batch workloads absorbed the rest.

What improvements typically look like

Results vary by topology, but partners commonly report:

Metric	Before	After
Effective capacity utilization	40–50%	65–80%
Cross-region job success	~90%	98–99%
SLA breach rate	3–4%	<1%

“We connected supply without forcing customers to move data. Once SLAs were enforceable, enterprise conversations became much simpler.” — Partner Ecosystem Lead

Why this becomes a business advantage

Federation is not just technical plumbing—it’s a commercial lever. TensorFusion helps turn fragmented GPU inventory into a single, managed compute market where SLAs are visible, enforceable, and scalable across clusters.