We size to the workload, not the invoice.
Nodes, not GPUs. Fabric matched to the job, since distributed training and production inference want different topologies. Prior-generation where it does the work.
Reserved capacity for training, fine-tuning and inference, sized to the workload, termed to your runway, and structured so it does not eat the capital you raised to build the model.
Nodes, not GPUs. Fabric matched to the job, since distributed training and production inference want different topologies. Prior-generation where it does the work.
A commitment that outruns your funded horizon is a liability dressed as a saving. We recommend the term that survives your next raise, even when it is the smaller deal.
Reserved and financed structures mean no hardware capex, no build-out, no depreciation risk. Time-to-compute in weeks rather than quarters.
Reserved versus on-demand, buy versus build, in a form you can hand to a CFO without translating it first.
Operators compensate us under referral agreements for qualified volume. You pay nothing incremental, the number you would get direct is the number you get, and that is exactly why we can tell you to buy less than you were about to.
What you are training, how big, how long, and what your funded horizon looks like.
With the two or three operators who fit, and the term structure we would actually sign.
We introduce and step back. No cost at any point.