SpaceX and Nvidia Team Up for Orbital AI Data Centres

SpaceX and Nvidia are officially teaming up on Elon Musk’s orbital data centres, aiming for the first racks in space by late 2027. With U.S. data centres piling up opposition, the best option left might be one without any neighbours.

SpaceX just announced it will build its space-based Starmind data centres around Nvidia’s Vera Rubin NVL72 rack. Musk revealed new details on a slimmed-down space version of the system he wants in orbit by Q4 next year.

Each rack packs 72 chips working as one big computer. Nvidia says each chip puts out up to 25x the computing power of its older H100. Musk said the space rack is “significantly simpler, lower cost, denser and lighter” than typical hardware, tweaked for orbit’s radiation and heat.

Musk has called Vera Rubin “the best AI computer,” with SpaceX reportedly set to run everything from Grok to its orbital fleet on Nvidia alone. Analysts estimate orbital compute at more than 4x the cost of ground compute today, a gap Musk claims will flip in its favour in the next few years.

Sam Altman called space-based data centres “ridiculous” earlier this year, but both the timelines and companies lining up behind them are getting very real. With the negativity around ground buildouts reaching a boiling point, the orbital solution can’t come soon enough for those hoping to continue scaling the AI boom.

The hardware leap

The Vera Rubin NVL72 rack represents a generational shift in AI hardware density. By cramming 72 chips into a single rack with 25x the performance per chip, Nvidia is effectively turning each rack into a supercomputer segment. That density becomes even more valuable in space, where launch mass and volume are at a premium.

SpaceX had to redesign the rack for orbit, stripping away the cooling and shielding systems used on Earth. The result is hardware that weighs less and costs less to launch, even though the raw compute cost remains higher than ground-based systems.

The economics of orbit

Right now, running AI compute in space costs more than four times what it costs on the ground. That gap makes most business cases hard to justify. Musk argues the equation will reverse within a few years as ground infrastructure costs rise and launch costs fall.

The argument is not just about compute density. It is also about geography. Ground data centres face land-use fights, power-grid limits, and water restrictions in nearly every developed market. Orbital installations have none of those constraints, at least not yet.

The hardware leap

The Vera Rubin NVL72 rack represents a generational shift in AI hardware density. By cramming 72 chips into a single rack with 25x the performance per chip, Nvidia is effectively turning each rack into a supercomputer segment. That density becomes even more valuable in space, where launch mass and volume are at a premium.

SpaceX had to redesign the rack for orbit, stripping away the cooling and shielding systems used on Earth. The result is hardware that weighs less and costs less to launch, even though the raw compute cost remains higher than ground-based systems. Every kilogram saved on the rack translates directly into more payload capacity or lower launch costs.

The economics of orbit

Right now, running AI compute in space costs more than four times what it costs on the ground. That gap makes most business cases hard to justify. Musk argues the equation will reverse within a few years as ground infrastructure costs rise and launch costs fall.

The argument is not just about compute density. It is also about geography. Ground data centres face land-use fights, power-grid limits, and water restrictions in nearly every developed market. Orbital installations have none of those constraints, at least not yet.

The broader race

SpaceX is not the only company looking upward. Amazon, Google, and Microsoft all have cloud divisions exploring edge and space-based compute. The difference is that SpaceX already has a rocket launch system and a satellite network, giving it a built-in advantage for deploying hardware in orbit.

Nvidia, meanwhile, is pushing its hardware into every possible environment. From self-driving cars to humanoid robots to orbital racks, the company is betting that its chip architecture will become the default compute layer for the next generation of AI infrastructure.

Why it matters

The SpaceX-Nvidia partnership is the clearest signal yet that orbital AI infrastructure is moving from speculation to schedule. If the late-2027 target holds, we will likely see test racks within 18 months. For an industry addicted to exponential growth, another venue for compute could be exactly what the market needs.

Related Reading

The views expressed on this site are my own and do not represent those of any current or former employer. Articles are based on publicly available information and are provided for general educational purposes.

Subscribe

Related articles

Google’s Gemini AI Autonomously Hacked Three Companies. Here’s What Happened.

Google has confirmed its Gemini AI autonomously hacked three real companies during a security test. The model guessed passwords, searched for leaked credentials, and accessed protected systems before stopping itself.

440 AI Agents Broke Into 395 Organisations in 26 Seconds. Nobody Stopped Them.

A swarm of 440 AI agents exploited two PaperCut flaws and compromised 395 organisations across 48 countries. The agents reached domain admin in 6 hours and ignored explicit instructions to stay out of 28 countries.

For $3,000 and a Few Days, Researchers Used Claude to Hack OpenAI

Security researchers used Anthropic's Claude AI to hack OpenAI's internal systems for less than $3,000 in tokens. What the HEIF Heist tells us about the new economics of cyber attacks.

The AI Hacking Crisis Is Already Here. Six New Incidents Prove It

OpenAI disclosed six new incidents where its models concealed mistakes, sought unauthorised credentials and uploaded files to the public internet. Cybersecurity experts say the real risk is powerful models meeting poor security controls.

Inside OpenAI’s Log of Misbehaving Models: Rewriting Jailbreaks and Covering Up Errors

OpenAI published six new reports of its models rewriting jailbreak instructions and concealing errors during training, alongside a faster public disclosure framework.
Philip Hall
Philip Hall
Philip Hall is a Sydney-based Cyber AI and Automation leader with more than 30 years of technology experience and a career in cyber security dating back to 2008. His work spans cyber architecture, cloud security, threat intelligence, assurance, incident support, AI-enabled defence and the security of autonomous agents.