Nvidia's latest $2 billion investment in chip maker Marvell isn't primarily about NVLink technology, as the headlines suggest. It's about securing Nvidia's dominance in the interconnect layer that binds together custom AI chips from companies like Amazon Web Services, Meta, and OpenAI. This deal, announced alongside similar $2 billion investments in optical component makers Lumentum and Coherent, reveals how Nvidia is strategically positioning itself across the entire AI infrastructure stack .

Why Is Nvidia Suddenly Investing Billions in Its Competitors?

On the surface, Nvidia's $2 billion commitment to Marvell appears to be a straightforward supply chain move. The company needs Marvell to ramp up production of custom processors and networking equipment that support Nvidia's NVLink Fusion ports, which allow different types of chips to communicate at high speeds. But the deeper story reveals Nvidia's recognition that the future of AI infrastructure won't be entirely homogeneous .

Marvell has become an AI datacenter company in its own right, much like Nvidia. The company serves as the primary chip shepherd for Amazon Web Services, helping design and package custom AI processors called Trainium and Inferentia chips. AWS has already announced that its future Trainium 4 processor will support both UALink and NVLink protocols, meaning it needs to work seamlessly with Nvidia's ecosystem while maintaining independence .

The $2 billion investment signals that Nvidia understands a critical reality: hyperscalers and cloud providers want to build their own custom chips to reduce costs and maintain control over their AI infrastructure. Rather than fight this trend, Nvidia is positioning itself as the essential connective tissue that makes these diverse systems work together .

What Technologies Is Nvidia Actually Providing Through This Deal?

The Marvell partnership explicitly includes support for multiple Nvidia technologies beyond just NVLink Fusion ports. Under the strategic partnership, Nvidia will supply supporting technologies for custom processors with NVLink Fusion ports, including a comprehensive ecosystem of interconnect and processing components .

• Vera CPUs: Nvidia's custom central processors designed for AI workloads that can integrate with third-party accelerators and custom chips.
• Groq LPUs: Language Processing Units from Groq that Nvidia is supporting through its interconnect standards, allowing these specialized inference chips to integrate into larger systems.
• ConnectX NICs and Bluefield DPUs: Networking interface cards and data processing units that handle data movement and security across AI clusters.
• NVLink interconnects and Spectrum-X switches: The physical and logical infrastructure that connects different types of processors together at high bandwidth and low latency.

This breadth of support suggests that Nvidia isn't just licensing a protocol; it's enabling an entire ecosystem where companies can mix and match different processors while maintaining compatibility with Nvidia's networking layer .

How Does This Reshape the AI Chip Landscape?

The Marvell deal raises a critical question that nobody in the industry has publicly addressed: who actually owns the rights to use NVLink protocols, and under what conditions? When a company like AWS buys NVLink hardware, do they automatically get the software rights to implement the protocol in their custom systems? The answer appears to be yes, but the terms remain murky .

Marvell's recent acquisition of Celestial AI in December 2025 for $3.25 billion adds another layer of complexity. Celestial's photonic fabric technology allows for row-scale coherent memory and in-network processing capabilities similar to what Nvidia offers through its NVSwitch chips. The question now becomes whether Nvidia will allow NVLink protocols to run on top of Marvell's photonic fabric, effectively letting Marvell compete with Nvidia's own networking solutions while remaining compatible with Nvidia's ecosystem .

This arrangement would represent a fascinating compromise: Marvell gets to use advanced photonic technology to build faster interconnects, while Nvidia maintains control over the protocol layer that makes everything interoperable. It's a model that could extend to other chip makers as well .

Steps to Understanding Nvidia's Interconnect Strategy in AI Infrastructure

• Recognize the shift from hardware to protocols: Nvidia is moving beyond selling GPUs to controlling the standards that allow different chips to communicate, giving it leverage across the entire AI infrastructure market regardless of which processor companies choose.
• Track Nvidia's $2 billion investment pattern: The company has made similar $2 billion investments in Lumentum and Coherent for optical components, and now Marvell for custom processors. This pattern shows Nvidia is systematically securing supply chains and partnerships across the entire stack.
• Monitor interconnect compatibility announcements: Watch for announcements about which custom chips and processors will support NVLink Fusion ports. Companies like Broadcom, which manufactures Google's TPUs and Meta's MTIA chips, may eventually need to negotiate similar deals with Nvidia.
• Understand the hyperscaler calculus: AWS, Meta, Google, and other hyperscalers want custom chips to reduce costs, but they also need to integrate with Nvidia's ecosystem because most customers expect to run Nvidia GPUs. Nvidia's strategy ensures it profits from both scenarios.

The Marvell deal also hints at potential future partnerships with Broadcom, Nvidia's rival in scale-out networking but also a supplier of custom chips for Google, Anthropic, Meta, ByteDance, Apple, and OpenAI. If any of these companies want NVLink Fusion ports on their custom processors, Nvidia and Broadcom will likely need to negotiate similar arrangements, further cementing Nvidia's position as the indispensable interconnect layer in AI infrastructure .

What makes this strategy particularly clever is that it doesn't require Nvidia to win every chip design competition. Instead, Nvidia wins by ensuring that whatever chips companies build, they'll need Nvidia's networking technology to make them work together efficiently. The $2 billion investments are essentially insurance policies that guarantee Nvidia remains central to the AI infrastructure ecosystem, regardless of how the custom chip market evolves .