Clouds Beyond the Atmosphere. AI-orchestrated orbital data centers—sun-powered, space-cooled.
The cloud is not weightless; it is materially intensive. U.S. data centers collectively withdraw ≈1.7 billion liters/day (with single hyperscale sites averaging ≈1.7 million L/day), and thermal management typically accounts for ~40% of electricity use. Meanwhile, European payment systems route on the order of €3 trillion/day through this infrastructure. We therefore examine an off-planet architecture—AI-orchestrated orbital data centers, powered by continuous solar and cooled via radiative heat rejection—to decouple compute growth from terrestrial constraints.
Quick glossary for key technical terms used in this poster.
Image AI Generated Inspired by Image from: Aili, A., Choi, J., Ong, Y.S. et al. "The development of carbon-neutral data centres in space." Nat Electron. (2025).
Placed here for attribution; styled subtly to avoid competing with glossary content.
U.S. data centers collectively withdraw ≈1.7 billion liters/day; single hyperscale sites average ≈1.7 million L/day. Thermal management typically accounts for ~40% of electricity use.
183 TWh consumed in 2024 (≈4% of total U.S. electricity). Cooling and infrastructure operations often represent ~40% of site energy draw.
105 million metric tons CO2 in 2024 (≈300% growth since 2018). Carbon intensity averages ~548 gCO2e/kWh across measured sites.
Placing compute in LEO keeps inference a few hundred–few thousand km from the sensor, yielding ~25–88 ms round-trip latency and ~50% faster free-space propagation than fiber—far below GEO. This shrinks the interval from data acquisition to user insight, enabling near-real-time edge analytics for constellations and cislunar operations.
Orbital edge systems pre-process data on-board (detection, compression, summarization), downlinking only salient products so terabyte-scale raw streams become kilobyte-scale telemetry. Federated learning exchanges model updates rather than datasets, further reducing downlink load and Earth-side storage.
An orbital cloud system exposes a network of compute/storage nodes reachable from ground stations and partner networks, decoupling access from local land, power, and water constraints. Workloads can be placed at—and retrieved from—the most efficient node via inter-satellite links.
Constellation architectures provide intrinsic redundancy: N+3–N+5 replication, ECC/TMR for radiation upsets, and autonomous failover that restores service in seconds rather than minutes. Mesh topologies and predictive routing sustain continuity despite single-satellite faults or regional ground outages.
AI-based routing evaluates network load, satellite geometry, and demand distribution to steer traffic over the best inter-satellite and ground links, minimizing latency and congestion. Predictive models anticipate failures or interference and proactively reroute flows to maintain QoS.
Using federated/distributed learning, each node performs onboard inference/training and shares model updates (not raw data), cutting downlink volume by orders of magnitude and preserving data privacy. Inter-satellite links synchronize parameters so the constellation adapts in near-real time and improves global model performance.
Onboard models conduct anomaly detection and fault diagnosis, triggering autonomous failover and recovery without waiting for ground intervention. In constellation architectures this enables seconds-scale service restoration and graceful degradation, supported by ECC/TMR and redundancy (N+3–N+5).
Ground Stations consume 4% of all electricity worldwide. Orbital Networks use clean solar energy.
Ground stations consume 450k gallons per day to cool processors. Orbital Networks leverage 3K space temperature to employ radiative cooling.
Ground stations rely partly on fossil fuels (1-5% of total emissions) for energy production.
Note: X-axis is logarithmic.
| Category | Terrestrial | Orbital | Moon |
|---|---|---|---|
| Radiator Eff (W/m²) | 100 | 1300 | — |
| Daily Temp Fluct (°C) | 5 | 0.001 | 150 |
| Annual Temp Var (°C) | 12 | 0.024 | 0.1 |
This is the cost breakdown of the costs associated with launching a singular datacenter in space. Even with the rapid cost reduction of rocket launches with SpaceX and Falcon 9, launch costs remain the primary barrier to entry into this new market segment.
Demonstration mission testing deployable radiator arrays and passive radiative cooling at 700–1300 W/m². Focus: thermal performance and long-duration material survivability.
Small-satellite cluster for on-orbit inference and federated learning. Objectives: validate low-latency routing, energy budgets, and autonomous workload placement.
Economic model comparing launch amortization, public-private finance mechanisms, and shared infrastructure partnerships to reduce CapEx barriers.
Terrestrial testbed replicating thermal vac conditions, power management, and AI orchestration to accelerate flight qualification and ops procedures.
AI systems monitor and repair satellite components without human intervention, reducing operational costs and response times.
Intelligent thermal management systems optimize radiative cooling and prevent overheating in the space environment.
Dynamic resource allocation and task scheduling across satellite constellations for optimal performance.
AI-powered security systems protect against space-based threats and ensure data integrity in hostile environments.
End of Poster