We build and run on thisH100 and A100 capacity, reached through the providers we build on rather than bought as a box. We size the card to the workload — and say so when a smaller one does the same job.
Not a boxed licence — we design and operate workloads here, and can place your committed spend through us.
NVIDIA’s Hopper-generation data-centre card — superseded by newer silicon, and still the card most training and large-model inference actually runs on.
Training runs, long-context inference, and models that will not fit comfortably anywhere smaller.
The previous generation — still widely available, and cheaper per hour.
Fine-tuning and batch inference where the deadline is not the binding constraint.
A smaller card built for inference and visual workloads.
Serving models in production where an H100 would sit half idle.
NVIDIA’s licensed software layer, with containerised microservices for serving models.
Organisations that want a supported inference stack rather than one they assemble themselves.
NVIDIA’s open-source serving layer for models in production.
Teams running several models on shared hardware.
NVIDIA does not sell you the hours — the provider running the silicon does, and that is where the pricing sits. Where capacity is placed through a partnership we hold, committed spend prices below the published hourly rate. We size the commitment against measured usage, because over-committing is the usual way the saving disappears.
When a node degrades or a driver stack starts misbehaving, we escalate through named contacts at the provider. When a node degrades or a driver stack starts misbehaving, we escalate through named contacts at the provider rather than the general queue. We cannot promise you a response time — that is the provider's to give, not ours.
NVIDIA and the clouds run credit and programme routes for early-stage companies. We prepare the application and put our name behind it. The award is the vendor’s decision, and we will not suggest otherwise.
We are not a hardware distributor and we do not sell you NVIDIA cards. If you want silicon racked in your own data centre, we will design and operate it, but the purchase goes through a hardware channel rather than us. We also do not have a private queue at NVIDIA — availability is set by the providers, and in a tight market it is the honest reason a start date slips. One of those providers is Podstack AI, which Anocloud’s CEO co-founded. We say so wherever we place your capacity, and we will benchmark another Indian provider on your own workload if you would rather use one. And many workloads do not need an H100 at all; where a smaller card or plain CPU is the right answer, we will tell you before you sign for a year of capacity.
Tell us what you are trying to do and we will come back with the sizing, the licensing and the price — from a partner, within two working days.
