Key Highlights
- Shares of Nvidia advanced 0.3% during premarket hours on Tuesday, trading at $202.74 and approaching the October peak of $207.
- Google plans to introduce its latest generation of tensor processing units at the Google Cloud Next conference in Las Vegas, created in collaboration with Marvell Technology.
- The new TPU lineup targets inference operations — where AI systems process user requests — contrasting with training workloads where Nvidia maintains dominance.
- John Vinh from KeyBanc retained his Overweight stance on Nvidia, setting a $275 price objective based on the company’s CUDA platform advantages.
- Major customers including Meta have committed multibillion-dollar agreements for Google TPUs, while Anthropic secured access to as many as 1 million chips, though availability issues persist.
The semiconductor giant has demonstrated impressive performance recently. Nvidia’s shares have surged 15% during the previous month, positioning the stock near its historic closing peak. This upward movement persisted Tuesday morning while Google prepared significant announcements regarding AI processing hardware.
Premarket activity showed Nvidia trading at $202.74, representing a 0.3% increase. The current price level brings the stock closer to its record closing mark of slightly above $207, achieved during October 2025.
The advance occurred as market participants anticipated quarterly financial reports from leading technology corporations. Investor sentiment toward Nvidia’s core operations appears increasingly positive.
Meanwhile, developments from competitors warrant attention. Google is poised to reveal a fresh iteration of its tensor processing units — TPUs — during the Google Cloud Next gathering in Las Vegas throughout the week.
Focus on Inference Capabilities
Reporting from Bloomberg indicates Google crafted its newest processors through collaboration with Marvell Technology. These chips emphasize AI inference: the operational phase where trained algorithms deliver responses to end users.
“The battleground is shifting towards inference,” Gartner analyst Chirag Dekate told Bloomberg. Google Chief Scientist Jeff Dean echoed that view, saying it now makes sense to specialize chips for either training or inference as AI demand grows.
Google has invested considerable effort in this direction over recent years. The TPU initiative has attracted substantial customers — Meta finalized a multibillion-dollar agreement to acquire TPUs through Google Cloud infrastructure. Anthropic similarly broadened its TPU allocation to potentially 1 million processors.
Google possesses a distinctive structural advantage in this arena. Among prominent AI developers, Google stands alone in manufacturing proprietary chips at scale, creating tighter integration between model development teams and hardware design groups.
The company has simultaneously expanded TPU accessibility. PyTorch developers gained TPU compatibility, and Google has allegedly tested on-premises TPU installations for corporate clients — representing a departure from its traditional cloud-exclusive approach.
CUDA Platform Strength
Financial analysts remain confident in Nvidia’s position. KeyBanc’s John Vinh reaffirmed his Overweight assessment on Monday, establishing a $275 price objective while emphasizing that the CUDA software ecosystem presents 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 wrote.
Nvidia CEO Jensen Huang has stated previously that his company’s processors enable applications “you can’t do with TPUs.” Interestingly, Google itself continues deploying Nvidia GPUs in conjunction with proprietary TPUs across AI initiatives.
Nvidia’s upcoming Vera Rubin architecture is anticipated to represent the most sophisticated AI hardware available upon release.
Availability constraints may complicate Google’s expansion strategy. An anonymous startup leader informed Bloomberg that TPU scarcity presented genuine difficulties, with restricted chip access beyond what Google allocated toward “the more elite teams.”
