According to a previous article from Wall Street Insight, 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 system, while Groq's founder and CEO Jonathan Ross, president Sunny Madra, and some core engineering personnel will join NVIDIA. Groq itself will remain independently operated, and its cloud business Groq Cloud will continue to provide services externally.
However, understanding this merely as a typical technology collaboration is clearly superficial. Technology can be licensed, but it is rare for the founder and core architecture team of a chip company to migrate as a &#34;side clause.&#34;
What NVIDIA truly values has never been Groq's revenue scale, but rather the architectural philosophy behind it. This philosophy is highly aligned with Google's TPU.
It is widely believed in the industry that as the focus of AI competition shifts from training to inference, the long-established dominance of GPUs is beginning to wane, and the advantages of TPUs in efficiency and cost structure are gradually becoming apparent, likely becoming a key moat for Google Cloud in the next decade. Against this backdrop, Jensen Huang has shown signs of anxiety for the first time, feeling cornered.
It is certain that once NVIDIA leverages this technology introduction to close the gap or even eliminate the difference with Google's TPU in inference architecture, the technological and ecological gap that has been widening between Google and the OpenAI/NVIDIA camp may quickly converge, and the competitive landscape will return to a tug-of-war state.
The next question is: will Google initiate its own &#34;code red,&#34; mobilizing all resources to attempt to block this deal, or will it respond more aggressively?
<h2>The acceleration of the inference era, TPUs are shaking the long-term dominance of GPUs</h2>
In the past year, Google's presence in AI infrastructure has undergone significant changes.
The advancement of Ironwood TPUs and the Gemini model system has transformed the competition between Google and NVIDIA from merely &#34;who bought more GPUs&#34; to a confrontation between two computing paths. GPUs still dominate training, but in the inference phase, which determines long-term costs and profit margins, TPUs are rapidly closing in or even surpassing.
This is not merely a simple comparison of performance parameters, but a concentrated reflection of architectural differences.
The advantage of GPUs comes from their general-purpose parallel computing capabilities, while TPUs have been highly customized ASICs for neural network inference since their inception. In terms of energy consumption per unit, controllability of latency, and the cost of large-scale inference, TPUs are more aligned with the real needs of the current commercialization phase of large models. As model capabilities stabilize, inference begins to consume more and more computing resources, making &#34;affordability&#34; more important than &#34;speed.&#34;
This is the root of NVIDIA's anxiety.
The AI narrative is transitioning from the training era to the inference era. Training is a one-time investment, while inference is a continuous expense; training determines the upper limit of capability, while inference determines the lower limit of business. When customers begin to seriously calculate long-term inference bills, the high premium model of GPUs faces structural challenges for the first time, while Google has internalized this challenge into its cloud business moat through TPUs.
**
From this perspective, the TPU is not just a chip, but a weapon of cost structure. It allows Google to gradually free itself from dependence on NVIDIA for cloud inference, giving Google Cloud a unique underlying advantage in the competition over the next decade.**
The value of Groq lies here.
<h2>Two Major Considerations for Acquiring Groq: Talent + Time</h2>
Groq was founded in 2016, and its founder Jonathan Ross was a former senior executive at Google Chips and one of the early core participants in TPU.
What Groq insists on is not the GPU-style general parallel route, but an architectural philosophy that emphasizes low latency, deterministic execution, and extreme inference efficiency. This philosophy is highly aligned with the design philosophy of TPU, yet there is a clear tension with NVIDIA's traditional GPU system.
This also explains why NVIDIA chose to &#34;acquire&#34; rather than &#34;develop in-house.&#34; Compared to building a completely new tensor architecture from scratch, directly absorbing the already validated TPU mindset is obviously faster and more realistic.
Previously, there were rumors in the market that NVIDIA would fully acquire Groq for as much as $20 billion. Although this was later denied, the rumor itself has already exposed NVIDIA's sense of urgency.
Groq's target revenue this year is about $500 million, and even if fully realized, it would be difficult to support extreme valuation multiples. What NVIDIA is willing to pay for has never been for financial reasons, but for time.
The final structure is a &#34;non-acquisition&#34; involving a technology license and core talent transfer. This not only reduces regulatory risks but also avoids solidifying the narrative of being &#34;forced to buy by TPU,&#34; but in essence, NVIDIA has already grasped the most critical capabilities.
This is a defensive counterattack.
<h2>The War for AI Infrastructure Has Changed</h2>
It does not mean that NVIDIA has lost the inference battle, but it clearly indicates that the dominance of GPUs is no longer taken for granted.
As inference becomes the main battlefield and cloud vendors begin to reshape cost curves with self-developed chips, NVIDIA must confront a reality for the first time: the future competition in AI infrastructure will no longer rely solely on larger GPUs.
And the real suspense still lies on Google's side.
If TPU continues to be deeply integrated with Gemini and becomes Google's most core differentiated capability, then this confrontation has just begun. NVIDIA's &#34;acquisition&#34; of Groq may be a signal—the door to the inference era has been opened, and even the dominant player must change positions in advance.

Reaching a technology licensing agreement with Groq and "incorporating" its core team is essentially NVIDIA's direct response to the rise of Google's TPU. As the focus of AI competition shifts from training to inference, the long-established dominance of GPUs is beginning to wane, and the advantages of TPUs in efficiency and cost structure are gradually becoming apparent, potentially becoming a key moat for Google Cloud in the next decade. In this context, Jensen Huang has shown signs of anxiety for the first time, feeling cornered

- Nvidia has recently entered a non-exclusive technology licensing agreement with Groq, integrating Groq's AI inference technology into its product lineup while retaining Groq's independent operations.  
- The collaboration signifies a strategic move for Nvidia, focusing on Groq's architectural philosophy that closely aligns with Google's TPU, as the competition in AI shifts from training to inference.  
- This partnership reflects Nvidia's urgency to address the competitive threat posed by Google's TPU, raising questions about Google's potential responses to protect its market position.

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Is NVIDIA in a hurry? Or is it being cornered by Google's TPU, with Jensen Huang willing to "recruit" Groq at any cost?