As artificial intelligence drives explosive demand for computing power, traditional data centers on Earth are hitting hard limits. They consume vast amounts of electricity, water for cooling, and land, while facing permitting delays, local opposition, and strained power grids. In response, a growing number of companies are looking skyward: placing data centers in orbit.
The concept of space-based or orbital data centers involves satellites equipped with high-performance computing hardware—servers, GPUs, and AI accelerators—powered by large solar arrays and cooled by radiating heat into the cold vacuum of space. These facilities would handle workloads such as AI inference, model training, or processing data collected by other satellites, with results beamed back to Earth via optical or radio links.
Space offers unique advantages that address terrestrial bottlenecks. In certain orbits, particularly sun-synchronous ones aligned with the day-night terminator, satellites can receive nearly continuous sunlight. Solar panels operate more efficiently outside Earth’s atmosphere, free from weather, night cycles, or atmospheric losses. This provides abundant renewable energy without competing for limited terrestrial grid capacity.
Cooling is another major draw. On Earth, data centers use energy-intensive air conditioning or water systems. In space, waste heat can be rejected passively through radiators into the near-absolute-zero environment of deep space, potentially eliminating much of the cooling infrastructure and its associated energy and water use. Proponents argue this could lead to lower long-term carbon footprints once launch emissions are amortized.
Additional benefits include reduced local environmental and community impacts on Earth, potential security advantages (harder physical access for certain threats), and the ability to process space-generated data closer to the source, cutting transmission volumes and latency for orbital applications.
The idea has moved rapidly from concept to concrete plans and early demonstrations.
Starcloud, a U.S. startup backed by NVIDIA and others, launched the first data-center-class NVIDIA H100 GPU into orbit in November 2025. It successfully trained a language model and ran inference workloads in space. The company has raised hundreds of millions of dollars and aims for a constellation of up to 88,000 satellites delivering around 20 gigawatts of compute capacity.
SpaceX has filed ambitious plans with the U.S. Federal Communications Commission for up to one million satellites dedicated to orbital AI compute, targeting significant capacity potentially starting as early as the late 2020s and scaling toward massive annual power levels. Blue Origin has proposed Project Sunrise with tens of thousands of satellites. Google’s Project Suncatcher explores flying Tensor Processing Units in tight satellite formations, with prototype launches eyed for 2027. Newer entrants such as Orbital have filed for constellations of up to 100,000 satellites aiming for multi-gigawatt capacity focused on AI inference. Other efforts include lunar data storage concepts and initiatives from China and Europe.
These systems typically envision large solar arrays and radiators (sometimes tens to hundreds of meters across), optical inter-satellite links for networking (often relying on existing constellations like Starlink), and specialized radiation-hardened or error-corrected hardware.
Engineering and economic hurdles are substantial. Launch costs remain a primary barrier. Even with reusable rockets, placing thousands or millions of heavy, power-hungry satellites into orbit is extraordinarily expensive. Viability often depends on further sharp reductions in cost per kilogram to orbit.
Heat rejection is deceptively difficult. While space is cold, radiating large amounts of heat requires extensive radiator surfaces, adding mass and complexity. Radiation in orbit degrades electronics over time, necessitating shielding, error correction, and higher replacement rates. Maintenance is nearly impossible with current technology; failed units would largely be written off. Latency for ground-to-orbit communication can limit certain interactive or tightly coupled workloads, though delay-tolerant tasks like batch inference or space-native processing fare better.
Broader concerns include orbital congestion and collision risk, increased space debris, interference with astronomical observations, and the visual impact of large constellations on the night sky. Launch and reentry emissions also factor into overall environmental accounting.
Orbital data centers are unlikely to replace terrestrial facilities in the near term. Most analyses suggest they will remain more expensive for many years, though the premium could narrow significantly if launch costs fall and satellite designs improve. They are better viewed as a complementary solution—particularly useful for AI inference, space data processing, or regions facing severe power constraints—rather than a complete substitute.
Success hinges on continued progress in reusable heavy-lift rockets, lighter and more efficient solar and radiator systems, radiation-tolerant computing, and high-bandwidth optical communications. Demonstrations in the coming years will test whether the physics and economics can align at scale.
If realized, space-based data centers would represent a profound shift: turning the orbital environment itself into part of the global computing infrastructure. The night sky may one day host not only stars and satellites for communication and observation, but also the silent, sun-powered engines of artificial intelligence. Whether this vision becomes a major reality in the 2030s or remains largely conceptual will depend on solving the practical challenges of building and operating compute at planetary scale beyond Earth.
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