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Nvidia is bankrolling AI accelerator supply chain buildout to reserve production capacity
Expanded supply to be available from 2027 through 2028
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On October 21, during GTC Berlin, Nvidia announced several massive, multi-billion-dollar deals with supply chain partners across substrates, advanced packaging, semiconductor testing, and optical engines to expand production capacity. Aimed at overcoming severe bottlenecks restricting the current and future AI infrastructure buildouts, the deals feature commitments and production roadmaps stretching through 2028, securing capacity for Nvidia and ensuring resiliency and redundancy in its AI accelerator supply chain. “We are in the midst of the largest infrastructure buildout in human history. Demand is extraordinary. Capacity is the constraint,” said Nvidia CEO Jensen Huang. “Our supply chain partners are the world’s best manufacturers. I’m grateful they are expanding capacity around the globe to scale production and help us deliver the AI supercomputing systems powering this new industrial revolution.”
The deals cover two major AI accelerator supply chain categories: hardware interconnects and OSAT (Outsourced Semiconductor Assembly and Test). Under hardware interconnects, Nvidia is securing capacity for both substrates and co-packaged optics. Nvidia has signed deals with Ibiden, Unimicron, and Samsung Electro-Mechanics — three of the world's five largest substrate manufacturers — to support its production capacity expansion and reserve significant portions of that capacity for the chipmaker. The company says it plans to secure 2.5x dedicated substrate capacity over the next three years. Nvidia has also signed agreements with SPIL (Siliconware Precision Industries Co., Ltd) to expand manufacturing capacity for the optical engines used in co-packaged optics by 15x by mid-2027.
On the OSAT side, Nvidia and Amkor are targeting a 300% increase in interposer wafer capacity by the end of 2028 through expanded advanced packaging capacity in South Korea, to support higher production volumes for Nvidia's platform. The company also plans to double its semiconductor testing capacity by 2027, investing $1.1 billion to reserve capacity from KYEC (King Yuan Electronics Co., Ltd.), the world's largest semiconductor testing company, which currently handles nearly all of Nvidia's chip testing.
Nvidia currently leads the AI chip market by a considerable margin, with its extremely powerful accelerators sitting in a vast majority of AI data centers globally. These deals target major bottlenecks in the AI chip supply chain, suggesting Nvidia doesn't plan to relinquish its position soon. So, why these specific deals? What are the exact details? What's the broader play? And where do Nvidia's competitors and the rest of the AI industry stand?
The substrate base, a specialized intermediary layer in every accelerator package that connects the tiny silicon chips above it to the board below, is one of the most critical bottlenecks in the AI chip supply chain. As we detailed in our comprehensive substrates roadmap, this indispensable physical layer, manufactured by only a handful of companies, is expanding in both the X-Y and Z axes as chip makers package an increasing number of components onto one board. Nvidia's Blackwell generation places two reticle-sized GPU dies and eight HBM3E stacks on a single package. Its Rubin and Rubin Ultra architectures mount even more components. As a result, each substrate now consumes much more material and production capacity, even as demand for individual substrates soars.
Fubon Research estimates that Nvidia would supply around 12.4 million data-center AI GPUs in 2028, up from a projected 8.2 million in 2026. Each one would feature a substrate layer. Nvidia is staying ahead of the curve, with a planned 2.5x capacity expansion over the next three years, led by Ibiden. Earlier this year, the company signed a deal to provide a multibillion-dollar capacity rese