For AI startups
Own the GPUs your product runs on.
Steady inference on rented GPUs means paying someone else’s margin every hour, forever. Own the baseline, rent the spikes, and keep your capacity when the market tightens.
Sound familiar?
What we hear, and what we do.
- Inference bills grow with every user
- Owned servers turn a per-hour bill into a fixed asset. More traffic on the same servers costs you nothing extra.
- Rented capacity gets repriced or recalled
- Your servers stay yours. No allocation queue, no renewal surprise.
- No one on the team should be racking servers
- We source, rack, power and run them at Yotta. Your engineers stay on the product.
- Cash matters
- Finance the purchase through your lender and depreciate it at 40% a year.
Recommended hardware
Where teams like yours start.
H200
Hopper · SXM
- Memory
- 141 GB HBM3e per GPU
- Power
- Up to ~10.2 kW per server
B200
Blackwell · SXM
- Memory
- 180 GB HBM3e per GPU
- Power
- Up to ~14.3 kW per server
RTX PRO 6000 Blackwell
Blackwell · PCIe Server Edition
- Memory
- 96 GB GDDR7 per GPU
- Power
- Up to 4.8 kW of GPU power for 8 cards, plus CPUs and fans
How it works for you
Own the baseline. Rent the peaks.
Size owned servers for the traffic you see every day, and burst to cloud only for launches and spikes. Most of the cost sits in the baseline, so that’s where ownership pays.
Questions
When should an AI startup buy GPUs instead of renting?
When a workload keeps GPUs busy most of the time for a year or more, typically production inference or continuous fine-tuning. Use our calculator to find your break-even.
Which GPU is best for LLM inference?
H200 for large models, thanks to 141 GB per GPU. RTX PRO 6000 for models that fit in 96 GB per card at lower cost and power. B200 when throughput per server matters most.
Can we start with one server?
Yes. Most teams start with one 8-GPU or 4U PCIe server and add more as traffic grows.
