By Matt Marshall
Publication Date: 2026-01-03 01:00:00
Nvidia’s $20 billion strategic licensing deal with Groq represents one of the first clear moves in a four-front fight over the future AI stack. 2026 is when that fight becomes obvious to enterprise builders.
For the technical decision-makers we talk to every day — the people building the AI applications and the data pipelines that drive them — this deal is a signal that the era of the one-size-fits-all GPU as the default AI inference answer is ending.
We are entering the age of the disaggregated inference architecture, where the silicon itself is being split into two different types to accommodate a world that demands both massive context and instantaneous reasoning.
Why inference is breaking the GPU architecture in two
To understand why Nvidia CEO Jensen Huang dropped one-third of his reported $60 billion cash pile on a licensing deal, you have to look at the existential threats converging on his company’s reported 92% market share.
The industry reached a tipping point in late 2025: For the first time, inference — the phase where trained models actually run — surpassed training in terms of total data center revenue, according to Deloitte. In this new “Inference Flip,” the metrics have changed. While accuracy remains the baseline, the battle is now being fought over latency and the ability to maintain “state” in autonomous agents.
There are four fronts of that battle, and each front points to the same conclusion: Inference workloads are fragmenting faster than…