Nvidia is getting anxious? Possibly cornered by Google TPU, Jensen Huang is determined to "acquire" Groq at any cost.

According to a previous article by Wallstreetcn, Nvidia recently reached a non-exclusive technology licensing agreement with Groq.

As disclosed, Nvidia will integrate Groq's AI inference technology into its future product lineup, while Groq’s founder and CEO Jonathan Ross, President Sunny Madra, and part of the core engineering team will join Nvidia. The Groq company itself will remain independently operated, and its cloud business, Groq Cloud, will continue to provide services externally.

However, to simply see this as an ordinary technology collaboration would be far too superficial. Technology can be licensed, but it’s rare for a chip company’s founder and core architecture team to migrate together as a “side condition.”

What Nvidia truly values is not Groq’s revenue size, but the architectural thinking behind it. And this set of ideas is highly homologous to Google’s TPU.

Industry consensus is that as the focus of AI competition shifts from training to inference, the long-standing dominance of GPUs is starting to loosen. TPU’s advantages in efficiency and cost structure are gradually coming to light, and are expected to become the key moat for Google Cloud over the next decade. Against this backdrop, Jensen Huang has, for the first time, shown signs of being backed into a corner and anxious.

It’s certain that if Nvidia, through this technology introduction, manages to close or even erase its gap with Google’s TPU in inference architecture, the widening technical and ecosystem rift between the Google and OpenAI/Nvidia camps could quickly converge, with the competitive landscape returning to a tug-of-war.

The next question is: will Google initiate its own “code red” and mobilize all resources to try to block the deal, or will it respond head-on in an even tougher way?

The Inference Era Accelerates—TPU is Shaking GPU’s Long-term Dominance

Over the past year, Google’s presence in AI infrastructure has noticeably changed.

The advancement of Ironwood TPU and the Gemini model system has shifted the competition between Google and Nvidia from “who bought more GPUs” to a confrontation of two computational paths. GPUs still dominate training, but in inference—which determines long-term cost and profit margin—TPUs are quickly catching up and even surpassing.

This is not simply a comparison of performance specs, but a concentrated manifestation of architectural differences.

The GPU’s strength comes from general-purpose parallel computing, while the TPU was custom-built as an ASIC for neural network inference from the ground up. In terms of power consumption per unit, latency control, and scaled inference cost, TPUs align more closely with the real needs of the current era of large model commercialization. As model capabilities stabilize, inference eats up more and more computing resources—“affordable computing” trumps “computing speed.”

This is exactly the root of Nvidia’s anxiety.

The AI narrative is shifting from the training era to the inference era. Training is a one-time investment; inference is a continuous expense. Training sets the capability ceiling; inference determines the commercial bottom line. When customers start seriously calculating their long-term inference bills, the GPU’s high-premium model faces its first structural challenge, and Google has precisely internalized this challenge into a cloud business moat via the TPU.

From this perspective, the TPU is not just a chip, but a weapon of cost structure. It enables Google to gradually eliminate dependence on Nvidia for cloud inference, giving Google Cloud a unique foundational advantage over the next decade.

This is where Groq’s value lies.

Two Major Considerations in Absorbing Groq: Talent + Time

Groq was founded in 2016, and its founder Jonathan Ross was formerly a Google chip executive and one of the early core participants in the TPU.

What Groq insists on is not the GPU-style general-purpose parallel path, but an architecture philosophy stressing low latency, deterministic execution, and extreme inference efficiency. This philosophy is highly homologous to the TPU's design, yet clearly in tension with Nvidia’s traditional GPU system.

This also explains why Nvidia chose “introduction” rather than “self-development.” Compared to building an entirely new tensor architecture from scratch, it’s obviously faster and more realistic to directly absorb the already proven TPU way of thinking.

Earlier, there were market rumors that Nvidia would fully acquire Groq at a price up to $20 billion. Though later denied, the rumor itself already exposed Nvidia’s sense of urgency.

Groq’s target revenue for this year is about $500 million; even if fully realized, it’s hard to justify an extreme valuation multiple. What Nvidia is willing to pay for has never been about finances—it’s about time.

The final structure landed as a technology licensing plus core personnel transfer—a “non-acquisition.” This not only reduces regulatory risk, but avoids confirming in public opinion that Nvidia was “forced by TPU to buy its way through.” But in essence, Nvidia has already gotten hold of the most critical capabilities.

This is a defensive counterattack.

The War Over AI Infrastructure Has Changed

This does not mean Nvidia has lost the inference battle, but clearly signals that GPU dominance is no longer a foregone conclusion.

When inference becomes the main battleground, and when cloud vendors begin to reshape cost curves with self-developed chips, Nvidia— for the first time— has to face the reality that future AI infrastructure competition cannot rely solely on bigger GPUs.

And the real suspense still lies with Google.

If the TPU continues to be deeply tied to Gemini and becomes the core differentiator of Google Cloud, then the confrontation is only just beginning. Nvidia’s “absorption” of Groq may be a signal—the gate to the inference era is now open, and even the overlord had to switch positions ahead of schedule.

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