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What NVIDIA’s latest filings show
NVIDIA’s fiscal year 2026 ended January 25, 2026. Its latest located quarterly filing covers Q2 FY2027, the quarter ended July 26, 2026. Keep these fiscal periods distinct from calendar years when comparing growth or describing results.
FY2026: rapid growth with a lower annual gross margin
| FY2026 measure | Reported result | Year-over-year change |
|---|---|---|
| Revenue | $215.938 billion | Up 65% |
| Data Center revenue | $193.7 billion | Up 68% |
| Gross margin | 71.1% | Down 3.9 percentage points |
| Operating income | $130.387 billion | Up 60% |
| Net income | $120.067 billion | Up 65% |
| Diluted earnings per share | $4.90 | Up 67% |
These figures are from NVIDIA’s FY2026 Form 10-K. The annual gross-margin decline was attributed to the transition from Hopper HGX products to full-scale Blackwell data-center systems and a $4.5 billion H20 excess-inventory and purchase-obligation charge in Q1 FY2026.
Q2 FY2027: acceleration in revenue and gross margin
| Q2 FY2027 measure | Reported result | Comparison |
|---|---|---|
| Revenue | $96.2 billion | Up 106% year over year; up 18% sequentially |
| Data Center revenue | $89.0 billion | Up 117% year over year; up 18% sequentially |
| Gross margin | 75.0% | 72.4% in Q2 FY2026 |
| Operating expenses | Not stated as a dollar amount in the cited Q2 FY2027 filing summary | Up 55% year over year |
NVIDIA’s Q2 FY2027 Form 10-Q attributed quarterly revenue growth to data-center products and said Blackwell continued to account for most system shipments. It attributed the Data Center increase to the ramp of Blackwell Ultra infrastructure and the gross-margin improvement over the year-ago quarter to Blackwell Ultra product mix. These are the company’s explanations of its results, not independent evidence that the same growth or mix will persist.
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How to evaluate NVIDIA’s valuation
A valuation multiple is meaningful only when its price date, denominator, and assumptions are clear. A fast-growing company can still be overvalued if its price assumes growth or profitability that does not arrive; a high multiple alone does not establish that it is mispriced.
- Fix the market-data date. Record the share price and source on the date of analysis, along with the share count used. Calculate market capitalization from that price and share count. If using enterprise value, state how it is derived from market capitalization and the balance-sheet items included.
- Label the earnings basis. For price-to-earnings, say whether earnings are trailing actual results or forward estimates. Identify the fiscal period, estimate provider, and date of the estimate. Do not present a forward multiple as though it were based on reported earnings.
- Cross-check the multiple. Price-to-sales can help when earnings are affected by unusual charges, but it ignores differences in profitability. Enterprise value to operating cash flow adds a cash-generation perspective, but it still depends on the period chosen and can move with working capital and investment needs. Explain what each measure does and does not capture.
- Test the implied expectations. Model more than one path for revenue growth, gross margin, operating expenses, customer capital spending, and AI-infrastructure demand. State which assumptions are responsible for most of the difference in your valuation outcomes.
- Compare peers only on matching terms. Use the same market-data date, fiscal periods, accounting basis, and valuation definitions, and name the peer group. A premium can reflect higher expected growth, but it also makes the result more sensitive to a shortfall in those expectations.
No October 2026 share price, analyst-consensus forecast, current valuation multiple, or peer data is established by the cited company filings. Those inputs are necessary to calculate a current P/E, price-to-sales ratio, or fair value; the historical operating figures above cannot substitute for them.
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Test whether growth is durable
Look beyond the headline revenue rate
Data Center accounts for the overwhelming share of the reported FY2026 business, so NVIDIA’s overall trajectory is especially exposed to AI infrastructure spending. Track Data Center growth alongside gross-margin direction, operating-expense growth, inventory provisions, customer concentration, and the pace at which customers actually deploy capacity. Revenue growth that depends on continued large-scale infrastructure investment should be assessed against the funding and buildout conditions that make those deployments possible.
Read supply commitments as both preparation and exposure
NVIDIA reported $279 billion of supply and capacity commitments as of July 26, 2026, up from $119 billion the prior quarter. The company said these commitments secure inventory and capacity for anticipated demand. They also increase the consequences of forecasting errors: NVIDIA warns that scaling production can create delays, quality issues, yield challenges, inventory provisions, and revenue volatility if demand or execution differs from expectations.
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Separate product-cycle effects from repeatable economics
The annual margin decline and subsequent quarterly margin recovery show why a single period should not be treated as a permanent margin level. Assess whether changes reflect a product transition, product mix, or a charge tied to a specific event, and then ask what margins might look like across a fuller product cycle. Also compare expense growth with revenue growth: in Q2 FY2027, NVIDIA said operating expenses rose 55% year over year, primarily because of higher compute-infrastructure and compensation costs.
Assess the main AI-business risks
Customer concentration and customers’ ability to finance deployment
One direct customer represented 16% of NVIDIA’s Q2 FY2027 revenue. Across the first half of FY2027, three direct customers represented 16%, 15%, and 13% of revenue, respectively, according to the Q2 Form 10-Q. These disclosures identify direct customers; they do not reveal every ultimate end customer. NVIDIA says some indirect customers may individually account for 10% or more of revenue and estimates indirect sales using purchase orders, product specifications, internal sales data, and other information.
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The filing also warns that some AI cloud providers and model developers may lack long-term infrastructure contracts or investment-grade financing capacity. If financing or deployment plans fail to materialize, orders can be delayed or reduced. NVIDIA further describes commitments to selected AI cloud partners that involve buying cloud services and may involve revenue sharing if stated criteria are met; changed market conditions could affect the results of those arrangements.
Power, data-center capacity, and execution
Customers need more than accelerators to put AI systems into service. NVIDIA identifies land, power, data-center shells, capital, and energy availability as important constraints, and says expanding capacity can take multiple years and involve regulatory, technical, and construction challenges. The company warned in its Q2 FY2027 filing: “The availability of land, power, shell, and capital is crucial to support the buildout of a full data center inclusive of NVIDIA AI infrastructure by our customers and partners, and any shortage of these or other necessary resources could impact our future revenue and financial performance.”
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This links demand for NVIDIA products to customers’ ability to secure and complete the surrounding infrastructure. Supply commitments and complex products add execution risk on NVIDIA’s side as well: shortages, manufacturing challenges, yield issues, or delays can affect the timing and cost of shipments.
Export controls and China access
NVIDIA’s Q2 FY2027 filing describes changing U.S. export-license requirements and restrictions, Chinese government actions, and limits on sales of certain data-center products. The company said H20 restrictions led to a $4.5 billion charge in FY2026, and that later H200 licensing did not mean all licensed products could be sold. As of July 26, 2026, NVIDIA said it was effectively foreclosed from competing in China’s data-center compute market. These are period-specific disclosures, not a guarantee of future policy or market access; reassess them when evaluating results from later periods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build a decision, not a prediction
A useful investment analysis makes the key dependencies visible rather than treating strong past performance as a forecast. For each valuation scenario, write down the assumptions about AI infrastructure demand, customer financing and buildout, NVIDIA’s ability to deliver supply, and the resulting revenue and margin path. Then identify which assumption would most change the valuation if it proved wrong.
If comparing NVIDIA with semiconductor or AI-infrastructure alternatives, use comparable evidence for revenue exposure, growth concentration, margins, operating expenses, cash generation, supply dependencies, infrastructure needs, software ecosystem and switching costs, geographic exposure, and export restrictions. The figures in NVIDIA’s filings alone do not establish how it ranks against peers on those dimensions. The available operating evidence supports a clear conclusion about the business’s recent momentum, but without dated market data and forecast assumptions it cannot settle whether the shares are cheap or expensive.
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