1
Unified Scheduler
Train and serve across heterogeneous accelerators.
Product
Distributed Cloud
Elastic GPU fabric for training and inference.
Multi-region
Fabric
GPU/NPU
Mixed fleets
SLA
Failover
Distributed Cloud is Zansoc’s compute fabric for AI workloads — scheduling training jobs and inference fleets across regions, providers, and edge sites. It embodies the patterns from our continental-scale inference research: routing, caching, failover, and deep telemetry.
Capabilities
Built for teams that ship.
2
Global Inference
Latency-aware placement and failover.
3
Cost Controls
Spot, reserved, and on-prem blending with budgets.
4
Telemetry
Kernel- to request-level observability.
In depth
01
One fabric, many accelerators
Teams submit jobs without rewriting for every cloud. The scheduler understands GPU generations, interconnect topology, and data locality. Inference packs can pin to edge sites when milliseconds matter.
02
Resilience as a feature
Provider outages and capacity cliffs are expected. Distributed Cloud drains traffic, warms standby replicas, and preserves SLOs with policies you can rehearse in game days.
03
FinOps with teeth
Budgets attach to projects and model channels. Idle capacity is reclaimed; wasteful jobs are surfaced with clear attribution so research velocity does not become an unlimited bill.
Next step