Nvidia’s Vera chip: The US$200 billion gamble Jensen Huang wants you to notice

The Nvidia Vera chip rarely grabs headlines when earnings exceed expectations, yet it deserves the spotlight. With Q1 revenue hitting US$81.62 billion—surpassing analyst estimates of US$78.86 billion—and a Q2 guidance of US$91 billion, well above Wall Street’s US$86.84 billion forecast, the figures did what Nvidia numbers always do: command attention.

However, buried in CEO Jensen Huang’s analyst conference call was a strategic revelation more significant than another quarterly beat. Huang revealed that Nvidia’s new Vera central processors open access to a US$200 billion market, entirely separate from the US$1 trillion projected from its Blackwell and Rubin AI GPU lineup between 2025 and 2027.

Huang anticipates Vera chip revenue reaching US$20 billion by the end of this fiscal year. “I expect (Vera) to be the second largest” sales contributor, Huang stated during the call.

This isn’t a footnote. It’s a second strategic front.

The Vera chip and the inference pivot

Nvidia’s need for a second front is clear: its largest customers are building their own solutions. Google, Amazon, and Microsoft—collectively projected to invest over US$700 billion in AI infrastructure this year, a sharp rise from around US$400 billion in 2025—are simultaneously funding custom silicon for AI model execution. Intel and AMD are also promoting CPUs as a viable option for inference workloads.

The industry narrative has shifted from who can train the largest model to who can serve it most cheaply and quickly. Inference is where Nvidia’s GPU dominance faces the greatest challenge. While training large models remains firmly in Nvidia’s domain, inference—generating answers at scale in real time—is increasingly where custom chips from Google’s TPU line, Amazon’s Trainium, and others are gaining traction.

Nvidia’s response is Vera. Developed partly using technology from Groq, an inference-focused startup Nvidia licensed in a deal reportedly worth around US$17 billion, this chip targets exactly this workload. The full Vera Rubin platform, combining the Vera CPU with Rubin GPUs, is scheduled for launch later this year.

Supply is already the constraint

Huang was transparent about one key issue: supply. “My sense is that we’ll be supply-constrained through the entire life of Vera Rubin,” he noted on the call. This admission is significant for a product Nvidia positions as a major growth pillar. To mitigate potential disruptions, Nvidia is investing heavily in its supply chain. The company reported that its supply commitments rose to US$119 billion in Q1, up from US$95.2 billion the previous quarter, a substantial increase reflecting both strong demand confidence and concerns about a global memory chip shortage.

Nvidia also announced an US$80 billion share repurchase program and increased its quarterly cash dividend to 25 cents per share, up from 1 cent, signaling financial strength even as Huang cautioned about tightening supply.

The question investors are asking

Despite beating estimates, Nvidia shares dropped 1.6% in extended trading. eMarketer analyst Jacob Bourne captured the sentiment: “Nvidia delivered another beat, but at this point that’s essentially priced in as it keeps beating quarter after quarter. The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel.”

Huang countered with his own data. He highlighted a growing segment of AI-specific cloud customers whose spending now roughly matches hyperscalers, but is growing faster quarter-over-quarter. “We should be growing faster than hyperscale capex,” he said.

The Vera chip is central to this argument. Whether the supply chain cooperates remains a separate challenge.

(Image source: Nvidia’s Newsroom)

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