Fermaca becomes Mexico’s first NVIDIA Cloud Partner, with a 60 MW goal for 2027

The announcement

The November 4 announcement (via EFE / PR Newswire) gives Fermaca a title no Mexican company has held: first NVIDIA Cloud Partner headquartered in Mexico. The first deployment has a name — Fermaca Digital Origins, in Mexico City — and a configuration: Blackwell Ultra clusters built on B300 NVL72 systems, with Vera Rubin on the roadmap. The division of labor is explicit: Inventec supplies the compute, DDN the storage, and Fermaca develops and operates. This is not a reseller badge; it is an operating model, with the Mexican company holding the keys to the plant.

The numbers deserve their labels. The 60 MW figure for the end of 2027 is a stated goal for total power — a target, not installed capacity — and the gigawatt belongs to Fermaca Digital City in Durango as an aspiration: up to 1 GW if the full project lands. Around the hardware sits a talent program with national reach: “México Aprende IA,” run with NVIDIA, trains university faculty — the unglamorous, decisive layer of any AI strategy, because factories need operators more than they need press releases.

What an AI factory actually sells

Strip the ceremony away and a cloud partner sells something beautifully concrete: finished computation, by the hour, without the customer building the plant. Owning an AI cluster means buying racks, securing power, installing liquid cooling, networking the machines, hiring the people who keep them alive and accepting that the hardware ages on your balance sheet. Renting from a cloud partner converts all of that into an operating expense and a service-level promise: the partner develops and operates — in this structure, that is Fermaca’s stated role — while partners such as Inventec and DDN supply the compute and the storage underneath. The customer’s engineers arrive with models and data; the factory’s engineers worry about everything below that line. It is the same division of labor that made the public cloud irresistible, rebuilt for machines an order of magnitude hungrier.

The hardware designation tells you the shape of the product. The NVL72 concept, which runs through NVIDIA’s current systems, treats the rack — not the individual server — as the unit of computing: dozens of accelerators wired by a high-speed interconnect into a single domain, so a large model can spread across them and still behave like one machine, with liquid cooling carrying away the heat that density creates. A deployment built on B300 NVL72 systems is therefore not a room of independent boxes to be divided casually among tenants; it is a small number of very large, coherent machines. That shapes who the natural customers are: organizations training or serving models big enough to need the whole domain, and willing to pay for coherence instead of assembling it themselves.

This is also why AI factories are announced in megawatts rather than in square meters. Floor space is rarely the binding constraint; power is. The electrical envelope of a site decides how many racks can run at once, which means a stated power goal is really a capacity budget: secure the megawatts, and the compute follows; fail to secure them, and the most elegant building in the country is a warehouse. Read that way, the ladder in this announcement — a first deployment in Mexico City, a stated goal for total power by the end of 2027, a larger campus aspiration in Durango — is a sequence of power decisions with buildings attached. Each step has to be financed, permitted and connected before a single additional rack can switch on, which is exactly why the labels “goal” and “aspiration” in the original announcement are not fine print. They are the honest description of work not yet done.

And then there is the layer that outlasts every hardware generation: people. A program that trains university faculty multiplies itself — each professor reached goes on to teach cohort after cohort of engineers, long after the first clusters are retired. Factories can be imported; operating cultures cannot. A country building both at once is playing the long game correctly.

The decision this sets

For Mexican enterprises and government, the decision landscape changes: sovereign, in-country AI capacity operated by a Mexican NVIDIA partner now exists as a purchasable option, with the latency, jurisdiction and contracting consequences that follow. For the region, Fermaca’s bet raises the standard question of every AI-factory announcement — execution against the 2027 goal — but the partnership structure (Inventec, DDN, NVIDIA) is the strongest available answer to it. For competitors, the first-mover fact is on the board: the Mexican seat at NVIDIA’s cloud table is taken.