SpaceX and Nvidia say they are adapting the Vera Rubin NVL72 rack-scale AI platform for orbital use, with SpaceX targeting a first launch in the fourth quarter of 2027. 

The dream of an AI data center in space lives on in SpaceX and Nvidia’s August 24 announcements that the platform for Low Earth Orbit (LEO) Starmind AI satellites will be based on the Vera Rubin NVL72 chip family and architecture.

This proposed system would form the computing core of SpaceXAI’s first-generation Starmind AI satellite and extend Nvidia’s architecture from terrestrial AI data centers into space. 

SpaceX CEO Elon Musk posted on X the same day, “SpaceX, in partnership with Nvidia, has designed a space-optimized Vera Rubin NVL72 system for launch to orbit in Q4 next year, with significant scale in 2028.”

SpaceX, in partnership with Nvidia, has designed a space-optimized Vera Rubin NVL72 system for launch to orbit in Q4 next year, with significant scale in 2028 https://t.co/qdDq8YBkzl

— Elon Musk (@elonmusk) August 24, 2026

Musk’s post came after he said during SpaceX’s Q2 earnings call, “Going forward, we’ve decided to build exclusively on Nvidia because we think the Vera Rubin architecture is the best architecture.” Musk continued, “This is not some sort of far-future, distant thing; we expect to start launching these next year. We think the design of the NVL72 VR computer is a much better design than, say, having a standard rack -style design. So we expect to deploy this on the ground as well as in orbit, because we think it’s going to be a radical simplification of the standard NVL72 rack. It will cost less. It will be more effective. If we’re going to put it in space, why not want to put it on the ground? I think that’s going to be pretty cool.”

On Earth, the Vera Rubin NVL72 is Nvidia’s rack-scale AI design that combines 72 Rubin GPUs and 36 Vera CPUs, alongside high-speed networking components such as ConnectX-9 SuperNICs. Nvidia says SpaceXAI’s planned Starmind satellite will be based on an optimized version of that system.

A conventional NVL72 rack assumes gravity, technicians, stable grid power, a building-scale liquid loop and frequent replacement of failed parts. Orbit removes each of these assumptions.

The idea is more ambitious than putting a conventional edge-AI accelerator aboard a spacecraft. Nvidia and SpaceXAI are proposing to bring a modified architecture used in AI data centers into orbit, while altering it for orbital operational requirements.

Getting that working in orbit, though, is easier said than done. 

As Curtis Pyke, founder of Kingy AI, writes, “A conventional NVL72 rack assumes gravity, technicians, stable grid power, a building-scale liquid loop and frequent replacement of failed parts. Orbit removes each of these assumptions.“

“Cooling is unforgiving. Space is cold, but vacuum does not carry heat away through convection.”

In particular, Pyke continues, “Cooling is unforgiving. Space is cold, but vacuum does not carry heat away through convection. Heat must travel from the chips to the radiator surfaces and then leave as infrared radiation. SpaceX says AI1 can avoid chillers, cooling towers and fans and reduce cooling overhead by an order of magnitude.”

SpaceX explains that AI1 would instead use closed-loop liquid cooling inside the spacecraft and large deployable radiators to send heat directly to space as infrared radiation. While the claimed reduction is physically plausible in principle, there’s no proof yet that these AI satellites’ cooling systems can deliver. 

Another major problem that remains unaddressed is how to make the orbital rack radiation-tolerant. Making Vera Rubin NVL72 radiation-tolerant means far more than putting an ordinary NVL72 rack in a shielded satellite enclosure. It would require a system-level redesign of its GPUs, CPUs, memory, networking, power, cooling, firmware, and operations around a specified orbit and mission life.

LEO orbit is not benign. NASA cites typical trapped-particle dose rates of 100 to 1,000 rad(Si) per year for low-inclination LEO spacecraft below 500 km. That level of radiation is not an immediate death sentence for electronics, but over a multiyear mission it will cause cumulative degradation. Radiation-qualified space hardware can deal with that. Commercial Off-The-Shelf (COTS) electronics are another matter. A true radiation-hardened Rubin GPU would also require design changes at the transistor and circuit levels. 

Even were Nvidia to make such a chip, for a high-density AI system such as the SpaceX design, the concern isn’t simply whether one processor survives a 5- or 10-year dose. The satellite contains numerous radiation-sensitive elements, such as GPU logic, SRAM caches, register files, system memory, and memory controllers. With thousands of cores and billions of memory storage cells, the aggregate fault rate — not the behavior of an individual component — drives the design.

The most realistic near-term answer would be a radiation-tolerant, fault-managed Rubin-derived orbital system, not a fully radiation-hardened NVL72 in the traditional military-space sense. It could use selected commercial Nvidia parts, substantial shielding, ECC and data integrity mechanisms, redundant controllers and power paths, aggressive fault detection, software recovery, and reduced-performance operating modes.

The post SpaceX designed an orbital Vera Rubin. Radiation comes next. appeared first on The New Stack.

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Half of enterprise AI deployments are missing their own latency targets at peak load. This is the headline finding of Akamai’s State of AI Inference 2026 report, which surveyed 200 AI practitioners and found that 82% of organizations say their most critical use cases require end-to-end response times of 500 milliseconds or less. A total of 64% of organizations now require end-to-end response times of less than 250 milliseconds for their most important use cases, yet 50% of deployments are failing to meet these latency demands at peak load.

My colleague Ari Weil, who leads product marketing for our cloud computing business and ran point on that research, summarizes the findings well: “The enterprise AI honeymoon phase is over… they are hitting the latency wall.” 

Agentic workflows aren’t a “single round trip”

The latency issue stems from the way agents work. It’s an iterative process, somewhat like a king sending out knights, emissaries, and messengers to conduct the business of the kingdom. There are many comings and goings, not just one person sent on a single round trip. 

For instance, when an agent built on a framework like LangChain, CrewAI, or Pydantic AI received a user request, it can fan out into dozens of sequential operations such as a reasoning call, a tool invocation, an API lookup, or a context retrieval. Then an agent may execute another reasoning call to decide what to do with what just came back. Every one of these operations or “hops” that must cross a wide-area network to reach a centralized data center adds latency, and a chain of 50 hops can multiply that transport time into seconds on its own, regardless of how fast the model generates tokens.

In fact, in a paper posted to arXiv in November 2025, researchers found that CPU-side processing can account for up to 90.6% of total latency in agentic workloads. In other words, your GPU might finish a reasoning step in a few hundred milliseconds, but then it might have to wait on additional tool call runs to CPUs in distant data centers. This is what causes spikes in GPU idle time. 

“More GPU capacity does nothing for this. You can’t brute-force your way out of a wait state.”

More GPU capacity does nothing for this. You can’t brute-force your way out of a wait state. This is the part of the conversation the industry keeps skipping, mostly because “buy more GPUs” is a much quicker fix to suggest than “figure out where your CPU-bound work is actually executing and why it’s so far from the data it needs.”

We need new benchmarks to fix the latency issue

One reason the looming latency wall sneaks up on teams is that they are not looking at the right benchmarks for agentic workloads. Most LLM-serving benchmarks measure tokens per second and GPU utilization on a single box. That’s great if the workload is indeed on a single box (i.e., one model answering one prompt), but that’s not the case with agentic workloads. Those benchmarks don’t address an agentic response that, say, makes a 50-hop chain cross a WAN 4 times to reach 4 separate services. 

“Staging may pass the benchmarks because it tests the model, but production tests the whole chain, including every hop your serving engine was never designed to see.”

That’s where the gap lies: Staging may pass the benchmarks because it tests the model, but production tests the whole chain, including every hop your serving engine was never designed to see.

The 500ms wall is not a soft target

This is showing up at scale because agents are moving into production faster than most teams’ architecture is evolving to support them. LangChain’s State of Agent Engineering 2026 survey of more than 1,300 professionals found that 57.3% of organizations now have agents running in production, up from 51% a year earlier. Among those builders, latency has become the second-most-cited barrier to production, behind only output quality. 

This is a serious issue for application teams. The 500ms threshold in Akamai’s survey isn’t a performance goal teams can afford to miss. For a live customer interaction or a real-time compliance check, that 500ms determines whether the application works or it doesn’t. 

We’ve solved this problem before

There’s a reason this feels familiar to anyone who was building for the web in 1999. Akamai exists because of a nearly identical problem. MIT researchers Tom Leighton and Danny Lewin founded the company to answer a challenge posed by Tim Berners-Lee: fix what the press had started calling the “World Wide Wait,” the crushing latency of pulling every request back to a small number of centralized servers. When the trailer for The Phantom Menace crashed sites across the internet in 1999, the culprit was distance: millions of browsers all reaching for the same far-away origin server at the same moment. The fix moved content to thousands of points closer to the people requesting it, instead of trying to build a faster origin.

Agentic AI is running into the same wall, just in a different vehicle. AI works just fine on centralized inference if you’re talking about running batch jobs overnight. But today’s applications built on agentic AI are real-time loops sitting inside live transactions, and the fix for agentic lag is distribution. Instead of expanding racks of CPUs and GPUs at the center, we need to move agentic execution to where the model’s tools, context data, and users actually live.

Agentic AI needs a tiered architecture, not a bigger data center

In practice, agentic AI requires a tiered architecture, one that includes a centralized core, regional GPU clusters, and CPUs at the Edge. 

• Centralized core—perfect for heavy reasoning over large context windows, where the round trip to a large model matters less than the model’s raw capability.
• Regional GPU clusters, increasingly built on hardware like NVIDIA’s Blackwell platform—ideal for localized inference, so the heaviest compute sits closer to where demand actually concentrates.
• Edge CPUs—the essential component for speed. This is the nexus for tool execution, orchestration, and context retrieval, since these are the steps that happen most often in a chain and benefit most from sitting next to the data and APIs they call.

We’ve built Akamai Inference Cloud around this tiered framework. It’s the same distribution logic behind our AI Grid Orchestrator. We route CPU-bound orchestration and tool calling to the edge, and keep GPU-bound reasoning where it makes sense, regionally or centrally. 

What to demand before you commit

The good news is you don’t need to distribute every workload to the edge on day one. But before you commit to a production architecture, you should know which of your agent’s dozens of hops are latency-sensitive and which aren’t. Then build a defined performance budget for each one.

“The teams that treat it as a GPU-shopping decision will be back here in six months, staring at the same four-second response time, wondering why more compute didn’t help.”

My advice is this: Before signing off on a large-scale inference deployment, ask your infrastructure for four things: 

1. Portability across regions and providers
2. Elasticity to absorb peak load without falling over
3. Data locality so tool calls aren’t crossing oceans to reach the context they need
4. A performance budget you’ve actually tested against production traffic, not staging traffic.

The teams that address this infrastructure decision now will be the ones whose agents still work when the benchmark environment transitions to real users. The teams that treat it as a GPU-shopping decision will be back here in six months, staring at the same four-second response time, wondering why more compute didn’t help.

The post Agentic AI has a latency problem that more compute won’t solve appeared first on The New Stack.

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Informational and operational technology data have long been treated as separate domains.

But AI changed the game. Today, you need the capacity to regularly ingest OT data into your IT systems without a hitch. (Or a breach.) You risk being left behind as your competitors put all their data to work, or assume the risk of consistently importing data from the edge to your internal systems. 

This problem is an immediate one for any company unwilling to be left behind in the AI era: If you want to take full advantage of AI, you need quick, ready access to relevant data. And if your physical operations have hit a snag, your digital tools need to be kept in the loop regularly.

The solution is not to build a host of custom scripts or depend on legacy FTP or SFTP solutions to bring data in from the edge. Those disparate tools can degrade, leak data, and fail during later, repeated OT data extraction runs.

Instead, engineers looking to free IT and OT data from their respective siloes are turning to a managed solution that offers strong encryption, continuous transfer monitoring, and the ability to fully audit every data handoff across the pipeline

Even more, OT systems — the Programmable Logic Controllers, Supervisory Control and Data Acquisition platforms, and historian databases running protocols like Modbus and OPC UA — were designed for uptime rather than connectivity. In modern architecture, however, no operational data can be left behind.

Getting data out of these environments means working against a connectivity model that was never meant to support the polling frequency or authentication patterns that modern IT infrastructure expects. Adding to the challenge, the more tools you introduce to free the OT data, the more attack vectors they may open.

A breach at the OT boundary can affect the physical systems those networks control. That’s a risk calculus most IT security frameworks weren’t built to handle.

On at 12 p.m. Eastern/9 a.m. On Tuesday, June 23, Fortra’s Jerrod Foster & Michael Barford will join The New Stack to discuss IT and OT systems, why extracting operational technology data is challenging, and how Fortra GoAnywhere MFT can resolve both data movement and data security issues that many engineers face today.

Register here to join the conversation:

What you’ll take away:

• Why the IT/OT boundary is an AI infrastructure problem: How the connectivity gap between operational and information technology creates a hard ceiling for teams building on live operational data — and what becomes possible when that data is reliably accessible inside modern pipelines.
• Where DIY solutions break: Why custom scripts and legacy transfer tools fail under real operational conditions — brittle transfers, no visibility, and attack surfaces you can’t audit.
• What secure OT data movement actually looks like: How Fortra GoAnywhere MFT provides an encrypted, automated, and auditable data movement layer that works with the constraints of real OT environments, not against them.

The post How to get operational data off the factory floor without creating an IT breach appeared first on The New Stack.

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