The numbers are staggering. Nvidia's latest AI racks hit $7.8 million to build. A single Rubin GPU? $50,000. Memory costs have surged 485% year over year. The hyperscalers are scrambling. But here's what they're not telling you: you don't need any of it.
Every quarter, the same cycle repeats. A new GPU generation drops. Prices double. The big cloud providers pre-order every unit before independent teams can even get a quote. By the time Rubin hits general availability, the waiting list will stretch into 2027.
This arms race isn't about performance — it's about control. The hyperscalers want you locked into their ecosystem, committed to multi-year reservations, paying for capacity you might never use. The $7.8M rack isn't a product. It's a moat.
Let's look at what actual AI inference workloads need:
An RTX 3090 at $0.18/hr running 24/7 costs $72/month. Compare that to an AWS p4d.24xlarge at $32.77/hr — that's $23,594/month. You're paying 327× more for hardware you probably don't need.
The RTX 3090 hit a sweet spot that may never come again: 24 GB GDDR6X, 936 GB/s memory bandwidth, CUDA 8.6 — all on a GPU that was mass-produced for gamers, not data centers. That means:
AI hardware is getting more expensive every quarter. But the workloads most teams actually run don't need a $7.8M rack. They need affordable, available compute with no lock-in and no surprises.
That's exactly what we built.
RTX 3090 compute at $0.18/hr. S3-compatible storage at $2.49/TB. Zero egress. No contracts.