Products / Layer 04
Distributed Compute
Coordinate AI workloads across Earth and orbital infrastructure. Federated learning, model partitioning, and bandwidth-optimized synchronization for hybrid Earth-space AI.
Why distributed Earth-space compute?
Large AI models don't fit on any single node. Training and inference must span infrastructure.
Bandwidth is scarce
Ground station passes offer limited windows. You can't send raw gradients - 100x compression is the difference between training and not training.
Latency varies wildly
LEO to ground: 5-40ms when visible. GEO relay: 240ms+. ISL hops add complexity. Model partitioning must account for dynamic topology.
Failures are normal
Radiation-induced upsets, eclipse power cuts, link dropouts. Distributed compute must continue despite partial failures.
See it in action
Watch federated learning coordinate across Earth and orbital infrastructure in real-time.
vs Naive Approach
Four capabilities
Primitives for coordinating AI across Earth and orbital infrastructure.
Federated Learning
Train models across Earth and orbital nodes without centralizing data. Our gradient-compress model achieves 100x compression with less than 0.5% accuracy loss - essential for bandwidth-constrained space links. Supports asynchronous aggregation for intermittent connectivity.
Model Partitioning
Run large models by splitting them across Earth and space. Our model-partition optimizer determines which layers run where based on latency, bandwidth, and compute constraints. Reduces end-to-end latency by 40% compared to naive placement.
Sync Scheduler
Intelligent data synchronization across ground station passes. Prioritizes what to sync based on data freshness requirements, available bandwidth, and upcoming connectivity windows. 45% improvement in sync efficiency over naive scheduling.
Space Mesh
Dynamic routing across inter-satellite links as constellation topology changes. Optimizes for latency, throughput, and reliability. Enables direct orbital-to-orbital data transfer without ground station bounces.
Use cases
Hyperscale AI Training
Train foundation models across ground and orbital compute. Solar-powered orbital nodes provide burst compute during sunlit phases. Federated aggregation handles intermittent connectivity.
For: Google, NVIDIA, hyperscale cloud providers
Edge AI at Scale
Run inference close to data sources - whether ground stations, aircraft, or ships. Model partitioning places compute where it minimizes total latency.
For: Defense, maritime, aviation
Constellation ML
Train models on satellite sensor data without downlinking terabytes. Federated learning keeps data in orbit while models improve continuously.
For: Earth observation, weather, imaging
Resilient Inference
Maintain AI services during ground infrastructure outages. Orbital nodes provide fault-tolerant inference when terrestrial systems fail.
For: Critical infrastructure, government
Research foundation
Built on our open research in distributed space AI.
5 Models
gradient-compress, model-partition, sync-scheduler, checkpoint-optimizer, bandwidth-predict
5 Datasets
Link Budget Archive, ISL Topology, Space Network Traces, Federated Training Logs, Checkpoint Recovery
Availability
| Capability | Status | Availability |
|---|---|---|
| Federated Learning | Research + Simulation | Q3 2026 |
| Model Partitioning | Research + Simulation | Q3 2026 |
| Sync Scheduler | Research + Simulation | Q3 2026 |
| Space Mesh | Research | 2027 |
Ready to build Earth-space AI?
Get early access to distributed compute capabilities. Start coordinating AI workloads across ground and orbital infrastructure.