The AI chip market is entering a new phase. No longer just a war of specs — who has more VRAM, who offers more TOPS — but a game played on the ground of ecosystems and alliances. The latest signal comes from Nvidia, which according to reports is intensifying a strategy of extensive collaboration with industrial partners, cloud providers, and integrators. It’s not a U-turn, but an acceleration of a trend years in the making: transforming from a mere silicon producer into the hub of a galaxy of actors revolving around the CUDA platform.

The underlying question is: why now? For years Nvidia could afford an almost vertical model, churning out ever more powerful GPUs and letting the market adapt. But with the explosion of generative AI, the landscape has changed. The demand for inference and training is so vast and diverse — from hyperscale cloud to the basement server — that no single company can cover all scenarios alone. And as AMD and Intel chase with alternative architectures, and custom chips (Google TPU, AWS Trainium) gain ground, collaboration becomes a tool to defend a dominant position.

Alliances help Nvidia embed its technology into every link of the chain. With major OEMs like Dell, HPE, and Lenovo, it ensures that servers with H100 or L40S GPUs are certified and ready for on-premise deployment, shortening the time for companies that want to bring AI behind the firewall. With cloud providers, it ensures its offering is the first choice for those renting capacity. With software and framework providers (think Databricks, Snowflake, or orchestrators like Kubernetes), it extends CUDA integration to the point of making it a de facto standard. The network effect is powerful: the more partners adopt the Nvidia ecosystem, the more costly it becomes for a customer to switch.

For those observing the on-premise world, the evolution is ambivalent. On one hand, the multiplication of certified appliances means that setting up a local cluster for LLMs is no longer a hobbyist’s adventure: you buy a validated node, install the drivers, and in a few hours you can run inference on quantized models. On the other hand, the dependence on the Nvidia stack deepens. If tomorrow a company wanted to replace GPUs with alternative accelerators, it would find that all the software — from runtimes to libraries — is optimized for CUDA, and migration would be a leap in the dark. This is the classic dilemma between immediate convenience and long-term technological sovereignty.

The alliance strategy also has a geopolitical dimension. Nvidia, under pressure from export restrictions to China, could use partnerships with local players to circumvent limits, offering down-clocked versions of its chips or software packages that run on local hardware. Here collaboration intertwines with data sovereignty: for governments and institutions, the possibility of having a certified Nvidia ecosystem managed by national partners could be an acceptable middle ground, though it doesn’t fully solve the problem of technological dependence on the United States.

Ultimately, the shift to a collaborative ecosystem is not a sign of weakness but the move of a leader seeking to lock in its advantage in an unforgiving market. For IT decision-makers, the message is clear: evaluating Nvidia adoption is no longer just a performance choice, but a buy-in to an entire value chain. Ignoring this dimension means mortgaging future flexibility.