When we talk about the AI revolution, few mention a harsh reality: over 85% of NVIDIA's datacenter GPUs are controlled by Microsoft, Google, AWS, Meta, OpenAI, and other tech giants.
This isn't just a statistic. It's an invisible wall.
Small and medium businesses, startups, and academic institutions — the organizations that should be engines of innovation — are being locked out of the AI revolution. Not because they lack ideas, but because they can't access or afford the monopolized compute resources.
demand and supply by 2030
doubling cycle
to build AI infrastructure
According to industry research, AI compute demand will exceed supply by 10x by 2030. GPU cost-efficiency only doubles every 3 years — far slower than Moore's Law predicted 18-month cycles. For a company wanting to build their own AI training environment, hardware alone starts at $60,000+ — not counting power, cooling, and operations costs.
A Simple Belief
You don't need to build a power plant to use electricity.
You shouldn't need to build a datacenter to train AI.
This is Orban's core belief: Compute should be as accessible as utilities — available on demand.
Look around and you'll notice something interesting: millions of GPUs worldwide sit idle. Gamers' high-end graphics cards sleep between sessions. Mining farms went quiet after Ethereum's shift to Proof of Stake. Enterprise IT equipment idles after hours. University compute clusters go unused during breaks.
If this distributed compute could be aggregated, it would form a massive network — powerful enough to reshape the AI industry's power structure.
The Distributed AI Compute Hub
Orban's role is to become the orchestration hub for this distributed network. We unify GPU resources from various sources, execute AI training and inference workloads, and provide enterprise-grade governance and security.
On the demand side, whether you're an SMB needing 24/7 AI customer service, a SaaS platform wanting to offer AI features, a university lab with limited resources, or an individual developer seeking plug-and-play GPU access — Orban matches you with the optimal compute resources.
Proof, Not Promises
In Llama 7B model training tests, we compared 3x RTX 4090s against a single H100 on 100,000 data points, achieving 40% cost reduction and 33% time savings.
This isn't theoretical — it's reproducible, verified results.
Why Now
Four factors converge to make this the perfect moment.
These four conditions aligning creates a rare market opportunity window.
Distributed AI Workloads
This is why Orban exists.