Nvidia (NVDA) Stock Approaches All-Time High as Google Launches AI Chip Offensive

Key Takeaways

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Nvidia shares advanced 0.3% in premarket activity Tuesday to $202.74, approaching its record closing price of $207 achieved in October.
Google plans to introduce its next-generation tensor processing units (TPUs) during the Google Cloud Next conference in Las Vegas, created in collaboration with Marvell Technology.
The new TPU generation targets inference operations—where AI systems process user requests—rather than the training phase where Nvidia maintains market leadership.
KeyBanc’s John Vinh reaffirmed his Overweight stance on Nvidia with a $275 price objective, highlighting the CUDA platform as a significant competitive advantage.
Major TPU deals include Meta’s multibillion-dollar commitment and Anthropic’s expansion to access up to 1 million chips, though availability issues persist for Google’s offerings.

Nvidia continues its impressive performance streak. The semiconductor giant’s shares have surged 15% in the last month and are closing in on record territory. This upward trajectory persisted Tuesday morning despite Google’s imminent announcements in the AI processor market.

NVIDIA Corporation, NVDA

Trading at $202.74 before market open, Nvidia posted a 0.3% gain. The stock is approaching its all-time closing peak of slightly above $207, reached in October 2025.

The upward movement arrived as market participants anticipated quarterly financial releases from leading technology firms. Investor sentiment surrounding Nvidia’s operations appears increasingly optimistic.

However, challenges loom on the horizon. Google is poised to reveal its newest tensor processing unit generation—TPUs—at this week’s Google Cloud Next gathering in Las Vegas.

Google Targets Inference Market

Reports from Bloomberg indicate Google engineered these latest processors alongside Marvell Technology. The chips prioritize AI inference capabilities: the operational phase where trained models deliver responses to user inquiries.

“The competitive landscape is pivoting toward inference,” Gartner’s Chirag Dekate explained to Bloomberg. Google Chief Scientist Jeff Dean reinforced this perspective, noting that chip specialization for either training or inference operations has become increasingly logical as AI demand escalates.

Google has strategically positioned itself for this moment over several years. Its TPU initiative now serves Meta as a significant client—the social network committed to a multibillion-dollar procurement agreement for TPUs through Google Cloud. Meanwhile, Anthropic broadened its TPU availability to potentially 1 million processors.

A structural advantage exists as well. Among prominent AI developers, none manufactures proprietary chips at volumes comparable to Google, creating tighter integration between model development teams and chip design operations.

Google has simultaneously democratized its TPU platform. PyTorch developers now have TPU access, and reports suggest the company has tested on-premises TPU installations for corporate clients—marking a departure from its traditional cloud-exclusive approach.

Nvidia’s Software Advantage

Wall Street analysts remain confident in Nvidia’s position. KeyBanc’s John Vinh sustained his Overweight recommendation Monday with a $275 price objective, contending that the CUDA software ecosystem establishes formidable obstacles for potential rivals.

“We see limited competitive risks and expect Nvidia to continue to dominate one of the fastest-growing workloads in cloud and enterprise,” Vinh stated.

Nvidia CEO Jensen Huang has previously emphasized his processors can execute applications “you can’t do with TPUs.” Significantly, Google continues deploying Nvidia GPUs in conjunction with proprietary TPUs for AI initiatives.

Nvidia’s forthcoming Vera Rubin platform is anticipated to represent the most sophisticated AI hardware available upon release.

Supply constraints may also impede Google’s expansion plans. An anonymous startup executive informed Bloomberg that TPU scarcity presented genuine challenges, with restricted chip availability beyond allocations Google directed toward “the more elite teams.”

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