Nvidia has delivered another blockbuster quarter, with second-quarter fiscal 2027 revenue reaching $96.2 billion, up 106% year over year and 18% sequentially, as demand for artificial intelligence infrastructure continues to accelerate. Data Center revenue was the main driver, rising 117% year over year to $89.0 billion, accounting for more than 92% of total quarterly revenue.
The results underline the scale of Nvidia’s position in the AI infrastructure boom. GAAP net income climbed 126% to $59.7 billion, while diluted earnings per share rose 128% to $2.46. Nvidia also returned approximately $26 billion to shareholders during the quarter and guided for third-quarter fiscal 2027 revenue of $108 billion, plus or minus 2%, without assuming any Data Center compute revenue from China.
Nvidia Q2 FY2027 Results At A Glance
Nvidia’s latest results show that the company’s growth remains heavily concentrated in AI-related computing infrastructure.
Revenue increased by more than $49 billion from the same quarter a year earlier, while Data Center revenue alone rose by approximately $48 billion.
Nvidia Q2 FY2027 Financial Results
| Metric | Q2 FY27 | Q2 FY26 | YoY Change |
|---|---|---|---|
| Revenue | $96.22 Bn | $46.74 Bn | +106% |
| Gross margin | 75.0% | 72.4% | +2.6 pts |
| Operating income | $63.73 Bn | $28.44 Bn | +124% |
| Net income | $59.69 Bn | $26.42 Bn | +126% |
| Diluted EPS | $2.46 | $1.08 | +128% |
| Data Center revenue | $89.0 Bn | — | +117% |
The numbers are based on Nvidia’s GAAP results for the quarter ended July 26, 2026.
Revenue Surges 106% To $96.2 Billion
Nvidia generated $96.221 billion in revenue during the second quarter, compared with $46.743 billion in the same quarter of fiscal 2026.
The sequential increase was also substantial. Revenue rose from $81.615 billion in Q1 FY27 to $96.221 billion in Q2, representing an 18% increase in only three months.
Q2 FY26
$46.7 Bn
↓
Q1 FY27
$81.6 Bn
↓
Q2 FY27
$96.2 Bn
↓
Q3 FY27 Guidance
$108.0 Bn ±2%This trajectory demonstrates the extraordinary pace at which Nvidia’s AI infrastructure business is expanding.
Data Center Revenue Hits $89 Billion
The most important figure in the earnings report is Nvidia’s Data Center revenue.
The segment generated $89.0 billion, up 117% from a year earlier and 18% from the previous quarter.
Data Center revenue represented approximately 92.5% of Nvidia’s total quarterly revenue.
Data Center’s Dominance
| Metric | Q2 FY27 |
|---|---|
| Total Nvidia revenue | $96.22 Bn |
| Data Center revenue | $89.0 Bn |
| Data Center share of revenue | ~92.5% |
| Data Center YoY growth | 117% |
| Data Center sequential growth | 18% |
The figures demonstrate how Nvidia has effectively transformed itself from a graphics-chip company into the central infrastructure supplier for the AI computing boom.
AI Infrastructure Demand Remains The Core Growth Engine
Nvidia CEO Jensen Huang said AI has reached an inflection point and that AI tokens are now productive and profitable.
His broader argument is that demand for computing is increasingly being translated into economic activity and revenue.
Huang said the AI infrastructure buildout is operating at full speed, with multiple frontier AI labs, startups, open-model developers and physical-AI companies scaling simultaneously.
Nvidia’s AI Demand Cycle
More AI Applications
↓
More AI Model Training
↓
More AI Inference
↓
More Compute Demand
↓
AI Data Centers
↓
Nvidia Accelerators
↓
Higher Nvidia RevenueThe breadth of AI demand appears to be increasing, rather than relying on a single major customer or AI laboratory.
Gross Margin Remains At 75%
Despite the enormous increase in revenue, Nvidia maintained a 75.0% GAAP gross margin.
That compares with 72.4% in the year-ago quarter and 74.9% in the previous quarter.
The ability to maintain such a high gross margin while ramping new infrastructure platforms is significant because large-scale semiconductor transitions can sometimes put pressure on profitability.
Nvidia Gross Margin
| Quarter | GAAP Gross Margin |
|---|---|
| Q2 FY26 | 72.4% |
| Q1 FY27 | 74.9% |
| Q2 FY27 | 75.0% |
| Q3 FY27 guidance | 74.0% ±0.5 pts |
Nvidia expects gross margin to moderate to approximately 74% in the third quarter, plus or minus 50 basis points.
Net Income Jumps 126%
Nvidia’s profitability increased almost as quickly as revenue.
GAAP net income reached $59.688 billion, compared with $26.422 billion in Q2 fiscal 2026.
That represents a 126% year-over-year increase.
Operating income was $63.734 billion, up 124% from $28.440 billion a year earlier.
Profitability Snapshot
Revenue
+106%
↓
Operating Income
+124%
↓
Net Income
+126%
↓
Diluted EPS
+128%The fact that profit growth exceeded revenue growth indicates strong operating leverage and sustained profitability.
EPS Rises 128% To $2.46
GAAP diluted earnings per share rose from $1.08 to $2.46, representing a 128% year-over-year increase.
Non-GAAP diluted EPS increased 120% to $2.22, compared with $1.01 a year earlier.
GAAP Vs. Non-GAAP
| Metric | GAAP | Non-GAAP |
|---|---|---|
| Revenue | $96.22 Bn | $96.22 Bn |
| Gross margin | 75.0% | 75.0% |
| Operating income | $63.73 Bn | $63.96 Bn |
| Net income | $59.69 Bn | $53.95 Bn |
| Diluted EPS | $2.46 | $2.22 |
Nvidia changed its non-GAAP methodology beginning in Q1 FY27 so that stock-based compensation is no longer excluded from its non-GAAP financial measures.
Nvidia Guides For $108 Billion Revenue
The company expects another major increase in the third quarter.
Nvidia’s Q3 fiscal 2027 revenue guidance is $108 billion, plus or minus 2%.
The midpoint would represent approximately 12.3% sequential growth from Q2’s $96.2 billion.
More importantly, Nvidia said its guidance assumes no Data Center compute revenue from China.
Q3 FY27 Outlook
| Metric | Guidance |
|---|---|
| Revenue | $108.0 Bn ±2% |
| Potential revenue range | ~$105.84–$110.16 Bn |
| Gross margin | 74.0% ±0.5 pts |
| GAAP operating expenses | ~$9.2 Bn |
| Non-GAAP operating expenses | ~$9.0 Bn |
| China Data Center compute revenue | Not assumed |
The China exclusion makes the guidance particularly notable because Nvidia is effectively projecting further growth without relying on that market in its baseline outlook.
Vera Rubin Platform Enters Full Production
Nvidia said its Vera Rubin platform is ramping into full production, with racks running at major infrastructure partners including CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure and Nebius.
Vera Rubin is intended to support the next phase of large-scale AI infrastructure.
The company’s ability to move from one architecture generation to another while demand remains exceptionally high is central to maintaining its growth trajectory.
Nvidia Platform Transition
Blackwell
↓
Vera Rubin
↓
Full Production Ramp
↓
AI Factory Deployments
↓
More Training + Inference
↓
Higher Compute DemandThe transition is also important because customers are increasingly building entire AI factories rather than simply purchasing individual accelerators.
Nvidia Expands Beyond GPUs
The latest results show Nvidia continuing to broaden its AI infrastructure portfolio.
During the quarter, the company unveiled Nvidia Vera, described as its first CPU built for AI agents, and said Nvidia Groq 3 LPX, an interactive AI inference accelerator, had entered full production.
Nvidia also launched its DSX platform, designed to provide infrastructure builders with a framework for designing, building and operating AI factories at scale.
Nvidia’s Expanding AI Portfolio
| Product / Platform | Role |
|---|---|
| Vera Rubin | AI computing platform |
| Nvidia Vera | CPU for AI agents |
| Groq 3 LPX | Interactive AI inference |
| Spectrum-6 | AI networking |
| BlueField-4 STX | AI storage processing/security |
| DSX | AI factory infrastructure platform |
| CUDA-X | Software libraries |
| Agent Toolkit | AI agent development |
This reflects Nvidia’s strategy of building a full-stack AI infrastructure ecosystem rather than competing solely on GPU performance.
Nvidia Targets The AI Inference Market
Training large AI models has historically been one of the primary drivers of accelerator demand.
But inference — actually running AI models to generate responses, recommendations or actions — is becoming increasingly important.
Nvidia’s Groq 3 LPX platform and broader inference software strategy indicate that the company expects inference demand to become a major source of future computing growth.
AI Model Training
+
AI Model Inference
+
AI Agents
+
Physical AI
↓
Expanding Compute Workload
↓
More AI InfrastructureThe company’s latest commentary suggests that the AI market is moving beyond model training toward widespread deployment.
Open Models Are Becoming More Important
Huang highlighted the growth of a “thriving open-model ecosystem” as one of the factors accelerating AI infrastructure demand.
Nvidia also announced new open-source models and software designed to help developers build autonomous AI agents.
This is strategically significant because Nvidia benefits from AI models regardless of which company develops them, as long as they require significant computing resources.
Closed Vs. Open AI Demand
| AI Ecosystem | Potential Nvidia Impact |
|---|---|
| Closed frontier models | High compute demand |
| Open models | Broad developer adoption |
| AI startups | New infrastructure demand |
| Enterprise AI | Inference growth |
| Physical AI | Robotics and autonomous systems |
The widening AI ecosystem could therefore make Nvidia’s customer base less dependent on a handful of frontier-model companies.
Nvidia Is Building The AI Financing Ecosystem
One of the more significant announcements was Nvidia’s partnership with major financial institutions to create independent compute financing platforms.
The company said it has announced strategic partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize more than $500 billion of third-party capital over time for AI infrastructure, subject to definitive agreements.
AI Infrastructure Financing
AI Compute Demand
↓
Need For Data Centers
↓
Huge Capital Requirement
↓
Financial Partners
Apollo
BlackRock
Blackstone
Brookfield
Goldman Sachs
KKR
↓
Potentially $500B+
Third-Party CapitalThis could help address one of the biggest constraints facing AI expansion: the enormous amount of capital required to build and power AI data centers.
Nvidia Returns $26 Billion To Shareholders
Nvidia continues to return substantial capital to investors despite its aggressive expansion.
During Q2 fiscal 2027, the company returned approximately $26 billion through share repurchases and cash dividends.
At the end of the quarter, approximately $99 billion remained available under Nvidia’s share repurchase authorization.
The company will pay its next quarterly dividend of $0.25 per share on October 1, 2026, to shareholders of record on September 10.
Capital Returns
| Item | Q2 FY27 |
|---|---|
| Shareholder returns | ~$26 Bn |
| Remaining repurchase authorization | ~$99 Bn |
| Next quarterly dividend | $0.25/share |
| Dividend payment date | Oct. 1, 2026 |
| Record date | Sept. 10, 2026 |
The scale of the buyback program demonstrates Nvidia’s ability to fund both rapid expansion and significant shareholder distributions.
Nvidia Generates $24.1 Billion Operating Cash Flow
Nvidia generated $24.077 billion in operating cash flow during Q2.
For the first six months of fiscal 2027, operating cash flow reached $74.421 billion, compared with $42.779 billion in the same period a year earlier.
Cash Flow Performance
| Metric | Q2 FY27 | Six Months FY27 |
|---|---|---|
| Operating cash flow | $24.08 Bn | $74.42 Bn |
| Year-ago period | $15.37 Bn | $42.78 Bn |
| Growth | ~57% | ~74% |
Strong cash generation provides Nvidia with considerable financial flexibility to fund research, acquisitions, investments, infrastructure and shareholder returns.
Nvidia’s Balance Sheet Has Expanded Rapidly
Nvidia ended the quarter with $320.27 billion in total assets, compared with $206.80 billion at the end of January 2026.
Current assets stood at $197.41 billion.
The company held $22.44 billion in cash and cash equivalents, $34.14 billion in marketable debt securities and $42.78 billion in marketable equity securities.
Nvidia Balance Sheet
| Item | July 26, 2026 |
|---|---|
| Total assets | $320.27 Bn |
| Current assets | $197.41 Bn |
| Cash & equivalents | $22.44 Bn |
| Marketable debt securities | $34.14 Bn |
| Marketable equity securities | $42.78 Bn |
| Total liabilities | $91.29 Bn |
| Shareholders’ equity | $228.98 Bn |
The company also reported $32.37 billion in long-term debt.
Nvidia Is Investing Heavily In Other AI Companies
The balance sheet also shows a significant increase in Nvidia’s equity investments.
Marketable equity securities reached $42.78 billion, compared with $12.89 billion at the end of January 2026.
Nvidia’s non-marketable securities stood at another $51.16 billion.
Together, these figures demonstrate how Nvidia is increasingly using its financial strength to participate in the broader AI ecosystem.
Edge Computing Revenue Reaches $7.2 Billion
Although Data Center dominates Nvidia’s results, the company’s Edge Computing business is also growing.
Second-quarter Edge Computing revenue was $7.2 billion, up 13% sequentially and 27% year over year.
The category includes AI computing closer to users and devices, including PCs, robotics and autonomous systems.
Edge Computing Growth
| Metric | Q2 FY27 |
|---|---|
| Revenue | $7.2 Bn |
| Sequential growth | 13% |
| YoY growth | 27% |
This business could become increasingly important as AI moves from centralized data centers toward devices, vehicles, robots and industrial environments.
Nvidia Pushes Into Physical AI
Nvidia is also expanding its AI strategy into robotics and autonomous machines.
During the quarter, the company introduced technologies including:
- NVIDIA Alpamayo 2 Super
- NVIDIA Cosmos 3
- NVIDIA Isaac GR00T reference robot
- NVIDIA Halos for Robotics
- Physical AI agent tools
These initiatives target autonomous vehicles, humanoid robots and other physical AI systems.
Physical AI Opportunity
AI Models
↓
Perception
↓
Reasoning
↓
Planning
↓
Robotics / Autonomous Systems
↓
Physical WorldThe strategy could create new sources of demand beyond conventional AI data centers.
China Remains A Major Uncertainty
One of the most important details in Nvidia’s outlook is its explicit exclusion of China Data Center compute revenue.
The company said its $108 billion Q3 revenue forecast does not assume any Data Center compute revenue from China.
This means the company’s near-term growth expectations are being built around demand elsewhere.
The China market remains strategically important, but Nvidia is not depending on it for the current quarter’s guidance.
Nvidia Is Expanding AI Infrastructure Globally
The company highlighted new partnerships and infrastructure activity across several regions.
Nvidia said it is expanding AI factory ecosystems in South Korea and Japan, while also reporting a record 35 new AI HPC supercomputers in development across Europe.
This reflects a broader trend toward countries developing sovereign AI infrastructure.
Global AI Infrastructure
| Region | Nvidia Activity |
|---|---|
| United States | AI factory expansion |
| South Korea | Sovereign AI infrastructure |
| Japan | National AI infrastructure |
| Europe | 35 new AI HPC supercomputers in development |
| Global | Vera Rubin production ramp |
The global diversification of AI infrastructure could provide Nvidia with additional growth opportunities.
The AI Infrastructure Buildout Is Getting Bigger
The most important takeaway from Nvidia’s results is not simply the $96.2 billion revenue figure.
It is the breadth of the infrastructure buildout behind that revenue.
AI demand now involves:
- GPUs
- CPUs
- Networking
- Storage
- Data centers
- Software
- Inference
- AI agents
- Robotics
- Financing
- Energy and power infrastructure
Nvidia is positioning itself across almost all of these layers.
What Nvidia’s Results Mean For The AI Industry
The results suggest that AI infrastructure spending remains extremely strong.
Data Center revenue growing 117% year over year means customers are continuing to invest aggressively in computing capacity.
The bigger question is how long this growth rate can continue.
Nvidia itself is guiding to another major quarter, indicating that demand remains strong enough to support continued double-digit sequential revenue growth.
The Bigger Picture
Nvidia’s second-quarter fiscal 2027 results provide another indication that the AI infrastructure boom remains in a high-growth phase. Revenue reached $96.2 billion, up 106% year over year, while Data Center revenue surged 117% to $89 billion. GAAP net income increased 126% to $59.7 billion, and the company maintained a 75% gross margin. The scale of Data Center revenue — roughly 92.5% of total quarterly revenue — shows how deeply Nvidia’s financial performance is now tied to global AI infrastructure investment.
The company is simultaneously expanding beyond GPUs into CPUs, networking, inference accelerators, software, AI agents and physical AI. Its Vera Rubin platform is entering full production, while Nvidia is also working with major financial institutions to potentially mobilize more than $500 billion of third-party capital for AI infrastructure. At the same time, Nvidia returned approximately $26 billion to shareholders and ended the quarter with around $99 billion remaining under its buyback authorization.
Looking Ahead
Nvidia’s immediate outlook remains exceptionally strong. The company expects $108 billion in Q3 fiscal 2027 revenue, plus or minus 2%, despite explicitly assuming no Data Center compute revenue from China. Gross margin is expected to moderate to around 74%, suggesting some normalization as new systems ramp, but the revenue forecast indicates that demand for AI infrastructure remains strong.
The longer-term question is whether Nvidia can maintain its extraordinary growth as AI customers move from initial infrastructure buildouts toward more efficient and mature deployments. For now, the company is betting that demand will broaden from model training to inference, AI agents, open models and physical AI. With new platforms such as Vera Rubin, Vera, Groq 3 LPX and DSX, Nvidia is positioning itself to capture a larger share of the expanding AI infrastructure stack
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