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NVIDIA and SK hynix both benefit from investment in AI infrastructure, but they make money at different points in the supply chain. NVIDIA sells accelerated-computing platforms and networking, and reported $89.0 billion in Data Center revenue in fiscal 2027’s second quarter. SK hynix supplies memory and storage products—including HBM, server DRAM and enterprise SSDs—that AI systems use; it reported 79.3187 trillion won in company-wide revenue for calendar Q2 2026. Those totals are not equivalent measures of AI revenue: they cover different businesses, reporting periods and currencies, and SK hynix does not report a standalone AI-revenue figure in the cited results.
They serve different roles in the AI supply chain
The simplest distinction is what each company sells. NVIDIA sells accelerated-computing platforms, including data-center compute and networking. SK hynix sells memory and storage products used in computers and data centers, including high-bandwidth memory (HBM), server DRAM and enterprise solid-state drives (eSSDs). AI data centers need both compute platforms and memory, so the companies are connected by customer demand without being interchangeable businesses.
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That difference matters when interpreting financial results. NVIDIA’s reported Data Center segment gives a distinct view of one major part of its business. SK hynix’s cited quarterly revenue is a company-wide total for its memory and storage business. Neither figure is a clean, directly comparable measure of “AI sales,” and only NVIDIA reports the cited Data Center figure as a separate revenue category.
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NVIDIA monetizes data-center investment through compute platforms and associated networking. Its fiscal 2027 second-quarter filing describes demand from hyperscalers, AI-native customers, enterprises and sovereign customers. That range of buyers helps explain why Data Center revenue reflects a broad data-center business rather than a separately audited line for AI-only products or workloads.
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For the quarter ended July 26, 2026, NVIDIA reported $96.2 billion in total revenue and $89.0 billion in Data Center revenue. Data Center revenue was up 117% year over year. These are company-reported figures from NVIDIA’s results release on August 26, 2026—not an independent estimate of the company’s AI-specific sales.
The distinction between a platform business and a component business is important. NVIDIA’s sale can represent a larger computing and networking deployment, while SK hynix’s sale can represent memory supplied into a server, system or facility assembled by another company. A single AI infrastructure project can therefore create orders for both businesses at different stages, with different contract terms and delivery schedules.
What SK hynix sells
SK hynix’s AI exposure comes through the memory and storage products that data-center operators and server makers need. The company’s Q2 2026 release described demand for high-performance products for AI servers and identified HBM as a leading product in that demand context. It also discussed DRAM and NAND, which serve broader memory and storage needs.
HBM is the product category most directly highlighted in the company’s AI discussion. Server DRAM and high-capacity modules support wider data-center workloads, while eSSDs provide enterprise storage. The product list shows why SK hynix can benefit from AI investment even though its financial reporting does not isolate an AI business. Demand can reach the company through several memory and storage products rather than through one “AI” ledger line.
SK hynix reported 79.3187 trillion won in total revenue for Q2 2026, in a release dated July 29, 2026. It said cumulative first-half 2026 revenue exceeded 100 trillion won for the first time. These are company-wide revenue figures; the release did not give a separate total for AI revenue. AI-server demand is a driver described by the company, not a boundary around all the revenue it reported.
How each company turns AI investment into revenue
AI infrastructure requires more than one product category. A data center needs computing capacity, connections between systems and memory to support workloads. The two companies participate in that buildout through different sales: NVIDIA through platforms and networking, SK hynix through memory and storage. A customer’s spending on an AI facility can therefore support demand for both without the companies selling the same thing or recognizing the same revenue.
NVIDIA: platform and deployment demand
For NVIDIA, the relevant question is how much customers spend on its data-center offerings and when those deployments translate into reported sales. Its Data Center category is useful because it separates that activity from the company’s other reported revenue categories. It is not, however, a pure measure of AI: the filing describes compute and networking activity and demand from several customer groups, rather than a standalone AI-only revenue line.
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The 117% year-over-year increase also needs a label. It is growth in NVIDIA’s reported Data Center revenue for fiscal 2027’s second quarter, not growth in a separately measured AI-only category. When comparing later quarters, readers should check whether NVIDIA changes segment definitions, reporting periods or presentation before treating the percentage as a continuous AI growth series.
SK hynix: memory demand and product mix
For SK hynix, AI investment affects demand for high-performance memory used in servers and data centers. The company’s Q1 2026 results provide product-mix context: it said sales increased for HBM, high-capacity server DRAM modules and eSSDs. Its later Q2 release reported the company-wide revenue total and discussed strong demand for high-performance AI-server products, including HBM.
Product mix matters because a company-wide total combines products serving different uses and customers. The cited releases do not quantify what portion of SK hynix revenue came from HBM, AI servers or AI workloads. It is fair to say that the company connected demand for high-performance products to AI-server investment; it is not fair to relabel all its memory and storage revenue as AI revenue.
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Why the revenue drivers can move differently
Their reported results respond to different immediate business drivers. NVIDIA’s Data Center results reflect demand and deployment of its compute platforms and related networking. SK hynix’s company-wide results reflect sales across memory and storage products, including product mix and the demand and pricing environment for those products. AI infrastructure is a shared demand source, but does not erase these differences.
As a result, the same broad expansion in data-center spending does not imply identical revenue timing or growth at both companies. Their sales depend on distinct products and reporting scopes. An NVIDIA platform shipment and an SK hynix memory shipment may be ordered together, but they can be delivered, recognized and priced under separate arrangements. The cited material does not provide enough detail to assign a precise share of either company’s revenue to a single AI customer, facility or workload.
What the latest reported figures do—and do not—show
| Company and period | Reported revenue | Scope and context |
|---|---|---|
| NVIDIA fiscal 2027 Q2, ended July 26, 2026; results released August 26, 2026 | $96.2 billion total; $89.0 billion Data Center | Data Center revenue grew 117% year over year. The segment includes data-center compute and networking; it is not a separately audited AI-only total. |
| SK hynix calendar Q2 2026; results released July 29, 2026 | 79.3187 trillion won total | Company-wide revenue across memory and storage; AI-server demand for high-performance products, including HBM, was described as a driver. |
| SK hynix first half of 2026 | More than 100 trillion won cumulative revenue | Company-wide first-half figure, not a standalone AI-revenue measure. |
| SK hynix Q1 2026 | 52.5763 trillion won total | Company-wide revenue; the company said sales increased for HBM, high-capacity server DRAM modules and eSSDs. |
The table’s figures come from company-reported results: NVIDIA’s fiscal Q2 release and SK hynix’s Q1 and Q2 releases. NVIDIA’s fiscal quarter ended July 26; SK hynix’s reported quarter is calendar Q2. The currencies and revenue scopes differ, too. For these reasons, putting $89.0 billion next to 79.3187 trillion won does not show which company had more AI revenue, greater AI exposure or a larger share of the same market. Currency conversion alone would not correct the scope and period differences.
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The growth rates also need their own context. NVIDIA reported 117% year-over-year growth for its Data Center revenue in fiscal 2027 Q2. The cited SK hynix Q2 release supplies a revenue total and describes demand conditions, but it does not provide a matching AI-revenue series to compare against NVIDIA’s segment growth. A comparison should not imply that this NVIDIA growth rate applies to SK hynix or that it measures the same activity.
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Reading the supply-chain connection step by step
A practical way to understand the relationship is to follow an AI deployment from demand to disclosure. First, an operator, cloud provider, enterprise or sovereign customer decides to add AI capacity. NVIDIA identifies those groups as sources of demand for its data-center business. The buyer then procures computing platforms and networking, along with servers and memory supplied by companies such as SK hynix. SK hynix’s releases point to HBM, high-capacity server DRAM and eSSDs as products whose sales increased or whose demand was associated with AI-server investment.
Next, each supplier records its own sale according to its contracts and accounting period. NVIDIA may report the resulting activity inside Data Center revenue. SK hynix records memory and storage sales in its broader company-wide revenue. The two entries can relate to one project without being equal in size, timing or scope. Finally, analysts must return to each company’s definitions before drawing a conclusion about growth or exposure.
This sequence prevents a common mistake: assuming that every dollar or won spent on an AI facility appears as AI revenue at every supplier. Spending can be distributed among compute, networking, memory, storage and other infrastructure. The cited releases establish the companies’ roles and reported totals, but not a complete bill of materials for any particular facility.
The companies are collaborators as well as suppliers at different layers
The connection is not only that both can benefit from data-center investment. In July 2026, SK Group and NVIDIA announced plans for a comprehensive AI infrastructure initiative valued at more than $500 billion. The announcement described a long-term SK hynix–NVIDIA partnership to secure and co-develop next-generation AI memory, including HBM. It also said the first planned AI factory was expected to come online in 2027.
The partnership gives the supply-chain relationship a concrete form: NVIDIA contributes the accelerated-computing platform side, while SK hynix works on the memory required for next-generation systems. It does not turn SK hynix into an NVIDIA segment or make their quarterly revenue lines interchangeable. Each company continues to report its own products, customers and periods.
This announcement is evidence of strategic collaboration and planned infrastructure, not proof that the full stated initiative is booked revenue, that all proposed capacity is complete, or that the factory is already operating. Its planned timing is forward-looking and may change. A planned 2027 AI factory should therefore be treated as an expectation in the announcement, not as 2027 revenue already secured in either company’s accounts.
A practical way to read future NVIDIA and SK hynix results
When a new quarter is reported, start with the scope of the figure rather than the headline amount. The following checks keep a supply-chain comparison from turning into a false apples-to-apples ranking:
- Identify the business measure. For NVIDIA, distinguish total company revenue from Data Center revenue. For SK hynix, identify whether the figure is company-wide or explicitly tied to a product category.
- Check whether AI revenue is actually disclosed. Do not treat a segment or full-company total as AI revenue unless the company defines and reports it that way. In the cited SK hynix releases, no standalone AI-revenue figure was provided.
- Keep the period attached. NVIDIA fiscal Q2 2027 ended July 26, 2026; SK hynix’s Q2 2026 is a calendar-quarter result. Results released on different dates can also reflect different reporting cutoffs.
- Keep currency and growth basis visible. Report dollars and won with the original figures, and label growth rates with the company, segment, period and comparison basis. Converting currencies does not make differently scoped figures comparable.
- Look at product context without overstating it. HBM, server DRAM and eSSDs help explain SK hynix’s exposure to AI infrastructure, while compute and networking help explain NVIDIA’s. Product mentions do not by themselves quantify revenue contribution.
- Separate plans from completed business. Announced partnerships and future AI-factory plans show strategic intent. They are not the same as operating capacity, recognized revenue or confirmed delivery.
- Track mix and pricing language. For SK hynix, note whether management discusses HBM, server DRAM, NAND or eSSD demand and pricing conditions. For NVIDIA, note whether commentary refers to hyperscalers, AI-native customers, enterprises or sovereign customers. These details explain drivers without creating an unsupported AI-revenue estimate.
- Compare like with like over time. A year-over-year Data Center comparison for NVIDIA should be kept separate from a quarter-over-quarter or full-company comparison for SK hynix. Write down the period and basis before calculating any change.
This method is useful for investors and anyone following the AI buildout because it separates exposure from measurement. A company can be closely tied to AI demand without reporting a distinct AI segment, while a reported data-center category can be highly relevant to AI without representing AI alone.
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What NVIDIA’s AI comments establish
In NVIDIA’s August 26, 2026 results release, founder and CEO Jensen Huang said: “AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue.” This is the CEO’s characterization of demand and the business opportunity. It is company commentary, not an independent market assessment or a measurement that changes the reported scope of Data Center revenue.
The quote helps explain NVIDIA’s platform framing: the company presents compute as an economically productive service rather than only as hardware. It should still be read alongside the reported figures and segment definitions. The statement does not establish a separate dollar amount for AI-only sales, nor does it describe the portion of SK hynix revenue attributable to AI servers.
Common comparison traps
Trap one: calling all NVIDIA Data Center revenue AI revenue. The segment includes data-center compute and networking and serves multiple customer groups. It is a strong indicator of data-center exposure, but not an audited AI-only total.
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Trap two: calling all SK hynix revenue AI revenue. The 79.3187 trillion won Q2 figure includes the company’s broader memory and storage business. HBM and AI-server demand are important context, not a reclassification of every sale.
Trap three: ranking the raw totals after currency conversion. Converting dollars to won can put the numbers in one currency, but it cannot align a segment with a company-wide total, or a fiscal quarter ending July 26 with a calendar quarter.
Trap four: treating the $500-billion-plus initiative as booked sales. The July announcement describes an extensive plan and a first AI factory expected in 2027. It does not confirm completed construction, recognized revenue or final capacity.
Trap five: inferring a precise product contribution from a product mention. Saying that HBM, server DRAM or eSSDs saw increased sales in Q1 2026 does not reveal their percentage of total revenue. Likewise, naming networking in NVIDIA’s Data Center category does not establish its share of that segment.
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Company revenue figures are often used to tell a simple story about who is “winning” in AI. Here, the headline totals can mislead if treated as comparable. NVIDIA’s $89.0 billion Data Center revenue is a segment figure in U.S. dollars for a fiscal quarter; SK hynix’s 79.3187 trillion won is a whole-company figure for calendar Q2. One is not a measure of the same market slice as the other.
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The more useful takeaway is about business exposure. NVIDIA sells computing platforms and networking that customers deploy in data centers. SK hynix sells memory and storage products whose demand is influenced by AI servers and data-center investment. Their financial statements show those activities at different levels of aggregation. For a personal-finance decision, that distinction is a starting point for understanding each company’s business—not, by itself, a basis for predicting returns or choosing a stock.
Readers should also remember that company commentary and announced partnerships are not guarantees. Huang’s statement describes NVIDIA’s view of AI demand; SK Group and NVIDIA’s July plan describes intended collaboration. Actual future results will depend on orders, delivery, product mix, pricing and the periods in which sales are recognized. The cited releases provide a factual snapshot of reported performance and strategy, not a forecast of investment returns.
Frequently Asked Questions
Are NVIDIA’s and SK hynix’s reported revenues directly comparable?
No. NVIDIA’s $89.0 billion figure is Data Center revenue for fiscal 2027 Q2, while SK hynix’s 79.3187 trillion won figure is company-wide revenue for calendar Q2 2026. They use different currencies, periods and scopes.
Does NVIDIA report AI-only revenue?
The cited NVIDIA release reports a Data Center category covering compute and networking and serving hyperscalers, AI-native customers, enterprises and sovereign customers. It does not present that $89.0 billion as a separately audited AI-only total.
Does SK hynix disclose a standalone AI-revenue figure?
No. SK hynix describes AI-server demand for high-performance products such as HBM and reports total memory and storage revenue, but its cited Q2 2026 release does not give a discrete AI-revenue number.
Why is HBM important in this comparison?
HBM is one of the high-performance memory products SK hynix identifies in connection with AI-server demand. Its inclusion shows how AI investment can reach a memory supplier, even though the company reports broader product revenue.
What did the July 2026 NVIDIA–SK hynix announcement include?
SK Group and NVIDIA announced plans for a more-than-$500-billion comprehensive AI infrastructure initiative. The announcement described an SK hynix–NVIDIA partnership to secure and co-develop next-generation memory, including HBM, and said the first planned AI factory was expected in 2027.
Does the partnership mean the announced amount is already revenue?
No. It is a strategic plan with forward-looking timing. The announcement does not establish booked revenue, completed capacity or an operating factory.
Which company is more exposed to AI?
The answer depends on the measure. NVIDIA has a separately reported Data Center category dominated by compute and networking, while SK hynix has company-wide memory and storage revenue influenced by AI-server demand. Their reported figures do not support a single apples-to-apples ranking.
How should currency conversion be used?
Keep NVIDIA’s dollar figures and SK hynix’s won figures visible with their original periods and scopes. Conversion can show an exchange-value approximation, but it cannot make a segment and a company-wide total measure the same business.
The Bottom Line
NVIDIA monetizes AI infrastructure mainly through accelerated-computing platforms and networking, while SK hynix monetizes it through HBM, server DRAM, NAND and enterprise SSD demand. NVIDIA’s $89.0 billion fiscal 2027 Q2 Data Center revenue and SK hynix’s 79.3187 trillion won calendar Q2 2026 company-wide revenue describe related but different slices of the supply chain. Read the companies as collaborators at adjacent layers—not as interchangeable AI-revenue peers—and keep each figure’s period, currency and scope attached.
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