Dedicated GPUs for training your own models
Full access to the hardware, no shared servers. We source capacity across providers at market price or below, and keep every server healthy with the software we build for GPU clouds.
Buying compute shouldn’t take a week of calls
The listed price is marketing. The capacity isn’t there when you get on the call. And when something finally fits, the minimum is three years.
Pricing
Prices that move
The advertised price is for a setup nobody actually uses. The real price only shows up after the second sales call.
Capacity
Capacity that isn’t there
GPUs listed as available often aren’t there when you ask. Training a model needs dozens or hundreds of GPUs at the same time.
Terms
Years-long lock-in
Direct access to the hardware is reserved for the biggest buyers. Everyone else gets multi-year contracts and a virtual machine between their code and the GPU.
You tell us the workload. We find the hardware
We’re not a marketplace. We source on demand across the providers we work with, which is why what we offer is actually available.
Cheap compute, without the usual trade-off
Sourcing
Routed to the best price
Every request goes to the provider with real capacity at the lowest rate. Market price or below, without a week of calls.
Bare metal
Direct access to the GPU
No virtual machines, no sharing servers with other customers. Full control of the hardware, ready for your training code.
Same layer
Kept healthy by Harbor
The same software we build for GPU clouds watches every server in your cluster. Problems are caught and fixed before they slow your training down.
For teams that need the whole GPU
Built for teams that need full control of their GPUs but can’t sign a three-year contract.

Large clusters
Foundation models
Pre-training needs hundreds of GPUs for a few months, not a few years. We source the capacity and hand you the full cluster.

Root access
Custom kernels
Write and tune your own low-level GPU code directly on the hardware. Nothing virtualized, full admin access on every server.

Own data
Domain models
Voice, radiology or anything where general models fall short. Train on your own data, on your own cluster.

Short terms
Fine-tuning
Serious hardware for runs that take weeks, not years. Start when you’re ready, hand it back when you’re done.