Nvidia's recent $20 billion strategic licensing agreement with Groq signals a significant shift in the artificial intelligence landscape, suggesting the era of general-purpose GPUs dominating AI inference is drawing to a close. The deal, announced in late 2025 and becoming apparent to enterprise builders in 2026, points towards a future of disaggregated inference architectures, according to industry analysts.

This move comes as inference, the process of running trained AI models, surpassed training in terms of total data center revenue in late 2025, a phenomenon dubbed the "Inference Flip" by Deloitte. This shift is placing new demands on silicon design, requiring specialized architectures to handle both massive context and instantaneous reasoning.

The licensing agreement indicates that Nvidia, holding an estimated 92% market share, is acknowledging the limitations of its general-purpose GPUs for the evolving demands of AI inference. Matt Marshall, reporting on the deal, noted that this is one of the first clear moves in a four-front fight over the future AI stack.

The rise of inference is driven by the increasing deployment of AI models in various applications, from autonomous vehicles to personalized recommendations. These applications require real-time decision-making based on vast amounts of data, pushing the boundaries of traditional GPU architectures.

The disaggregated inference architecture involves splitting silicon into different types, each optimized for specific tasks. This allows for more efficient processing of AI workloads, potentially leading to lower latency and higher throughput.

Nvidia's investment in Groq, a company specializing in Tensor Streaming Processors (TSPs) designed for high-speed inference, suggests a strategic move to adapt to this changing landscape. TSPs offer an alternative to GPUs, focusing on minimizing latency and maximizing performance for specific AI models.

The implications of this shift are far-reaching, potentially impacting the entire AI ecosystem. As enterprises increasingly adopt disaggregated inference architectures, new players and technologies are expected to emerge, challenging Nvidia's dominance. The next few years will likely see intense competition and innovation as companies vie for position in this evolving market.