Nvidia designs its AI GPUs, but making them at scale depends on a network of specialist suppliers. In its fiscal 2026 Form 10-K, Nvidia identifies TSMC and Samsung as wafer foundries, says it uses CoWoS semiconductor packaging, names SK hynix, Micron, and Samsung as memory suppliers, and lists contract manufacturers involved in assembly, testing, and final-product packaging. The practical point is that an AI GPU is not just a logic chip: its performance and production depend on bringing compute, high-bandwidth memory, and other components together in a complex manufacturing chain.
Does Nvidia manufacture its own AI chips?
Nvidia designs its GPUs and systems, but the company’s fiscal 2026 Form 10-K describes an outsourced production chain for the semiconductor wafers and other manufacturing steps. It names TSMC and Samsung as foundries producing its semiconductor wafers. It also names independent subcontractors and contract manufacturers that handle assembly, testing, and packaging of final products.
That distinction matters: a company can design a chip without owning the factories that make its wafers or performing every step needed to turn chips and memory into finished products. Nvidia’s filing identifies suppliers and stages, but does not assign every supplier to every GPU model.
How the supplier chain fits together
The disclosed chain spans wafer fabrication, advanced packaging, memory, and final assembly and testing. The table reflects what Nvidia says about its supply relationships; it is not a ranking of supplier scale or importance.
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| Stage | What Nvidia discloses | What the disclosure does not establish |
|---|---|---|
| Wafer fabrication | Nvidia identifies TSMC and Samsung as foundries that produce its semiconductor wafers in its fiscal 2026 Form 10-K. | It does not break out which foundry makes each GPU model or the share of production assigned to either one. |
| Advanced packaging | Nvidia says in the same filing, “We utilize CoWoS technology for semiconductor packaging.” | The filing does not quantify CoWoS capacity, per-GPU packaging allocations, or a supplier-by-supplier split for packaging. |
| Memory | Nvidia names SK hynix, Micron, and Samsung as memory suppliers. | The filing does not identify which supplier provides memory for each GPU generation or disclose equal or specific supplier shares. |
| Assembly, testing, and final-product packaging | Nvidia names Hon Hai, Wistron, and Fabrinet among the independent subcontractors and contract manufacturers performing these activities. | The list does not specify which company handles a particular product or the proportion of work performed by each. |
What is CoWoS packaging, and why does it matter?
CoWoS is the semiconductor packaging technology Nvidia says it uses. In this context, advanced packaging is the step that integrates chip components and memory into a tightly connected package. It is a separate manufacturing dependency from wafer fabrication: making a logic die is not the same as packaging it with the components needed for a finished high-performance processor.
That integration is important for AI GPUs because their compute capability must be paired with high-bandwidth memory (HBM). The packaging stage enables components to be brought together at high density, while HBM supplies the high-bandwidth memory needed by these systems. Nvidia’s filing confirms use of CoWoS, but does not detail every package design or show which specific GPU uses which packaging arrangement.
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It is therefore reasonable to treat advanced packaging as a key part of the production chain, but not to claim from the cited filings that CoWoS is the bottleneck for a particular GPU. Nvidia does not publish a CoWoS-only capacity figure or a per-model packaging allocation in the disclosures described here.
Who makes the memory in Nvidia AI GPUs?
Nvidia’s fiscal 2026 Form 10-K names SK hynix, Micron, and Samsung as memory sources. This establishes a multi-supplier relationship at the company level; it does not mean all three supply every GPU generation, or that their contributions are equal.
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Nvidia and SK hynix announced a multiyear partnership on June 7, 2026, to advance next-generation memory aligned with Nvidia’s AI infrastructure roadmap. Nvidia said the work includes memory for Vera Rubin AI supercomputers and other platforms. The announcement is evidence of collaboration and roadmap planning, not a public shipment breakdown by supplier or product.
Nvidia has also described collaboration with Samsung across HBM3E and HBM4, memory, foundry services, chip design, computational lithography, and factory operations. In its announcement, Nvidia reported 20x performance gains for specified computational-lithography and technology-computer-aided-design simulations. That is a company-reported result for those workflows—not a GPU performance claim or an independently verified manufacturing outcome.
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Why AI GPUs need high-bandwidth memory
AI GPUs perform large volumes of computation, and they need data to move between memory and processing resources quickly. HBM is the high-bandwidth memory component in this supply chain. Its role helps explain why Nvidia identifies memory suppliers alongside foundries and packaging: the finished system depends on more than the compute die alone.
Memory supply is also a manufacturing-planning issue. Nvidia’s supplier disclosures and its partnerships with memory companies show that memory availability and future product roadmaps are connected. They do not, however, reveal the memory cost per GPU, the amount of HBM in a particular model, or how much supply each memory company contributes.
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What Nvidia’s latest cited filing says about supply constraints
In its Form 10-Q for the quarter ended July 26, 2026, Nvidia reported that its supply and capacity commitments had risen from $119 billion in the prior quarter to $279 billion. The company described those commitments as relating to data-center infrastructure systems and primarily to memory and manufacturing facilities. The $279 billion figure is a broad company commitment—not an amount for CoWoS alone, HBM alone, or any individual supplier.
The same filing said Blackwell accounted for the majority of system shipments, Vera Rubin had begun production shipments, and Nvidia was experiencing certain supply constraints. Those are statements about the situation reported for that filing period, not timeless descriptions of product mix or supply conditions. The filing does not apportion the constraints among packaging, memory, foundries, or other inputs.
Where Nvidia’s manufacturing network is concentrated
Nvidia said in its fiscal 2026 Form 10-K that its supply chain was mainly concentrated in Asia, while the company was expanding into the United States and Latin America. It also cautioned that scaling in new locations depends on local ecosystems reaching the required volumes on time. This means geographic diversification is not simply a matter of moving a factory: the surrounding suppliers and manufacturing capabilities must also be able to support production at the needed scale.
What this means for readers assessing Nvidia’s business
The supplier chain helps explain why GPU production depends on multiple specialized inputs and why changes in demand or manufacturing capacity can matter beyond the company that designs the chip. Foundry access, packaging, memory, and final assembly all form part of the path from design to finished system.
- Supplier names show exposure, not allocation. Nvidia’s filings identify companies in its supply chain but do not disclose shares of a particular GPU’s production or cost.
- Capacity commitments are not a supplier-level measure. The $279 billion reported as of the quarter ended July 26, 2026, spans broad data-center infrastructure commitments, primarily for memory and manufacturing facilities.
- Product transitions make dated statements important. The filing’s comments on Blackwell, Vera Rubin, and supply constraints describe the period reported, not a permanent state.
For investors and other business readers, the disclosures support understanding Nvidia as a designer whose production relies on a complex supplier ecosystem. They do not by themselves quantify the exposure to any individual supplier, prove that one manufacturing stage is the current bottleneck, or predict the effect of a supply constraint on future financial results.
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