Running AI workloads in classified or export-controlled environments means your compute platform cannot depend on external networks, cloud APIs, or vendor-managed infrastructure. An air-gapped HPC platform operates in complete isolation, with every component required to schedule, execute, and monitor jobs packaged on-site. As a result, that constraint changes how you evaluate hardware, software stacks, and

• HPC & Defense
• Inference & Infrastructure
• Sovereign AI

From autocomplete to frontier intelligence Not that long ago, you were typing an email and the app suggested what you were going to type next. You may have wondered: “How did it do that?” At the time, very few people realized that the ability to make those suggestions would create the world we live in

• Cost & Pricing
• Inference & Infrastructure
• Sovereign AI

The growing demand for AI and advanced simulation tools for nuclear medicine research presents significant challenges in scalability and research efficiency. Hoonify partnered with NorthStar to deliver the TurbOS® software platform, which included a complete suite of performance-optimized research tools that produced faster Monte Carlo N-Particle (MCNP) simulation results. To eliminate support challenges and reduce

• Community & Impact
• Sovereign AI

Why Sovereignty Comes Before Speed A nuclear criticality safety analyst at a DOE laboratory. A radiation transport specialist modeling shielding at a DOE site. A materials scientist running molecular dynamics at a national lab. All three are solving different problems. But the infrastructure question underneath them is the same one. It is not “how much

• HPC & Defense
• Sovereign AI

Where self-hosting starts to pay Self-hosting an open model beats serverless pricing per token, but only if you can keep the hardware full. One owned eight-GPU node breaks even around 2.8 billion output tokens a month of the work it can actually serve, which is roughly 180 engineers running agentic workflows or 550 on lighter

• Cost & Pricing
• Inference & Infrastructure

Eighteen months ago, our biggest worry about AI was whether it actually worked. Today, our biggest worry is whether we can predict what it costs. That shift from “can we build it” to “can we afford it at scale” has quietly become one of the most consequential financial decisions a scaling company makes. We’re not

• Cost & Pricing