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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallAs of August 18, 2026, NVIDIA’s next earnings report is scheduled for August 26; the results are not yet available. The latest reported figures show a business increasingly powered by AI data-center infrastructure, while the next major product transition is from Blackwell to Vera Rubin. The key questions are whether demand converts into profitable deployments, whether customers can earn adequate returns on their spending, and whether NVIDIA can execute the transition without a slowdown.
What is confirmed as of August 18, 2026?
NVIDIA’s latest reported results are for the fourth quarter and full fiscal year 2026, released in February. Its next report, for the quarter ended July 26, 2026, is scheduled for Wednesday, August 26, at 5 p.m. Eastern (2 p.m. Pacific). NVIDIA says written CFO commentary will be posted with the results before the conference call. Until that release, Q2 fiscal 2027 revenue and performance remain unknown. NVIDIA’s earnings announcement
The most recent confirmed business figures and guidance are:
| Measure | Latest confirmed figure | How to read it |
|---|---|---|
| Q4 fiscal 2026 Data Center revenue | $62.3 billion | Reported quarterly revenue |
| Fiscal 2026 Data Center revenue | $193.7 billion | Reported full-year revenue |
| Fiscal 2026 Gaming revenue | $16.0 billion | Reported full-year revenue |
| Fiscal 2026 Professional Visualization revenue | $3.2 billion | Reported full-year revenue |
| Q1 fiscal 2027 revenue outlook | $78.0 billion, plus or minus 2% | Company guidance, not reported revenue; excluded Data Center compute revenue from China |
These results make NVIDIA’s business difficult to assess as primarily a gaming-GPU company. Data Center is its economic center of gravity, spanning accelerators, CPUs, networking, systems, software and services. The figures and outlook above are from NVIDIA’s fiscal 2026 results.
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- OC mode: 2632 MHz (OC mode)/ 2602 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- A 2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis
A reporting change investors should account for
NVIDIA said it would include stock-based compensation expense in its non-GAAP financial measures beginning in fiscal 2027. That changes the basis for comparing some non-GAAP figures with earlier periods. When reading the August report, distinguish GAAP results from NVIDIA’s revised non-GAAP figures and from analyst-adjusted estimates rather than treating them as interchangeable.
How Blackwell, Blackwell Ultra and Vera Rubin fit together
Blackwell and Blackwell Ultra
Blackwell is NVIDIA’s current-generation accelerated-computing platform. Blackwell Ultra is a higher-performance variant aimed particularly at reasoning and agentic-AI workloads. NVIDIA has claimed that Blackwell Ultra can deliver up to 50 times better performance and 35% lower cost for agentic AI in specified comparisons. These are company claims tied to particular comparisons, not universal guarantees for every workload. NVIDIA’s fiscal 2026 results presentation
Vera Rubin
Vera Rubin is the next-generation platform for large-scale AI training and inference. NVIDIA describes it as comprising six new chips, bringing together components for compute, networking and complete systems. The company says Rubin can reduce inference cost per token by up to 10 times compared with Blackwell. That is an NVIDIA claim; actual cost depends on workload, configuration, utilization, power, software and system-level costs, and the claim should not be treated as an independently verified result across production environments. NVIDIA’s Rubin announcement
Rank #2
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5070 Ti
- Integrated with 16GB GDDR7 256bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
NVIDIA has named AWS, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure among providers expected to deploy Vera Rubin-based instances. An announced deployment partner establishes neither the scale nor timing of deployments, pricing, customer utilization or profitability. The earnings call may help clarify whether Rubin is adding to demand or leading some customers to defer Blackwell purchases while they wait for the next platform.
What could keep NVIDIA’s momentum going?
The growth case rests on customers continuing to build AI infrastructure for training, inference and increasingly complex agentic systems. NVIDIA sells more than a chip: its competitive proposition also includes CUDA and other software, networking, rack-scale systems, developer familiarity and broad cloud availability. A customer evaluating an alternative must compare the cost and performance of a useful workload, not just theoretical chip specifications.
- Inference expansion: If AI services reach more users and serve more requests, demand may extend beyond the initial build-out of training infrastructure.
- Platform breadth: Networking and complete systems can matter to deployment performance and purchasing decisions alongside accelerators.
- Cloud access: Named cloud-provider plans could make new platforms accessible without customers buying and operating their own data centers, though availability and commercial terms vary.
- Software ecosystem: CUDA, libraries and developer experience can reduce migration friction for existing users, but the switching cost depends on workload, frameworks and the customer’s own software stack.
Those are reasons demand could persist, not proof that every customer will earn a return on its investment. NVIDIA’s statements about accelerating demand reflect management’s view and are forward-looking. The economic test is whether capacity is installed, used effectively and monetized.
Rank #3
- Powered by the NVIDIA Blackwell architecture and DLSS 4. System Requirements: Minimum 850W PSU with 16-pin 12V-2x6 (12VHPWR) connector required. Verify before purchasing.
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability. Compatibility: 348mm (13.7") length, 3.6 slots, 4.3 lbs. Confirm case clearance and slot spacing. GPU bracket included.
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.6-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
What could weaken the outlook?
Demand is not the same as returns
Four questions are often collapsed into one: Are customers ordering systems? Are systems being installed on schedule? Are they being used efficiently? Are the resulting services or productivity gains worth the capital spent? Strong supplier revenue answers the first question more directly than the last three.
Useful evidence includes hyperscaler capital expenditure, cloud GPU rental prices and utilization, inference cost per token, model economics, power availability, data-center financing and customer concentration. Reporting on Wall Street financing plans for AI data centers points to a potential source of additional funding and leverage across the ecosystem; it does not establish that NVIDIA has a liability or that an industry crisis is underway. Axios’s August 12 report on AI data-center financing
Competition and customer alternatives
Competition comes from AMD Instinct and Intel Gaudi accelerators, as well as Google TPU, Amazon Trainium and Inferentia, Microsoft’s custom silicon, and Chinese suppliers subject to technical and regulatory constraints. Customers can also use their own ASICs, mixed architectures, more efficient models or software optimization to reduce the amount of compute required.
Rank #4
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5060
- Integrated with 8GB GDDR7 128bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
These products are not interchangeable in every workload. The relevant comparison includes framework compatibility, software support, networking, availability, deployment time and total cost per useful training job or inference—not simply a peak-performance figure. CUDA is a significant ecosystem advantage, but it is not an insurmountable barrier: switching costs vary, and cloud providers can build software stacks around their own silicon.
Export controls and China
U.S. export rules have affected NVIDIA’s H20 products and previously produced inventory, purchase-obligation and revenue consequences. In fiscal 2026 commentary, NVIDIA described an approximately $8 billion revenue impact from H20 restrictions in its Q2 fiscal 2026 outlook. That is a historical figure, not an estimate of current impact. NVIDIA’s SEC-filed CFO commentary
NVIDIA’s fiscal 2026 outlook assumed no Data Center compute revenue from China. That assumption should not be mistaken for a statement of current policy or future shipments. Export rules are government policy; a possible license is not a guarantee of sales, and Chinese customers or authorities may favor domestic alternatives. The August 26 report and later government announcements will be more relevant to the company’s current assumptions.
Best Value
- Powered by the NVIDIA Blackwell architecture and DLSS 4 OC mode: 2640MHz/Default mode: 2610MHz (Boost Clock)
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.125-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
Supply, execution and expectations
Even with strong demand, revenue depends on production and deployment execution across advanced packaging, high-bandwidth memory, networking, system integration, data-center construction and available power. A transition to a new rack-scale platform adds execution questions; product announcements alone do not establish shipment volume or recognized revenue. Expectations also matter: a strong reported number may not satisfy investors if guidance, margins or deployment signals fall short of what the market already anticipates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to watch in NVIDIA’s August 26 report
Use the report to test the business mechanics, not just whether revenue beats an estimate. Consensus expectations are not company guidance, and a “beat” alone does not show whether customer spending is sustainable.
- Revenue and guidance: Actual Q2 fiscal 2027 revenue, Q3 outlook, full-year trajectory, Data Center growth and China assumptions.
- Margins: GAAP and revised non-GAAP gross margin, product mix, and the costs of increasingly complex systems.
- Platform transition: Blackwell and Blackwell Ultra demand, Rubin production and customer deployment dates, and signs that Rubin demand is additive rather than displacing near-term Blackwell purchases.
- Customer base: Evidence of demand beyond the largest cloud providers, plus the nature of multi-year commitments and partner activity.
- Supply and deployment: Constraints in memory, packaging, networking, system integration, power and construction capacity.
- Cash and commitments: Capital commitments, strategic investments, share repurchases and dividends, considered alongside cash generation.
How different readers can use the news
Investors
Compare results and outlook with prior company guidance, then examine Data Center growth alongside customer capital spending, gross margin alongside system mix, and Rubin demand alongside Blackwell demand. Also track China assumptions, customer concentration and cash generation relative to commitments. The investment question is not answered by a revenue headline alone; it turns on whether growth, margins and customer economics can support expectations.
Enterprise buyers
Compare systems on availability, delivery lead time, compatibility with your frameworks, support, power and cooling, networking requirements, and cost per useful workload. Decide whether cloud access or on-premises ownership fits the workload and operating model, and assess vendor lock-in and the migration path to future systems. A theoretical performance ratio is not a substitute for a workload-specific cost and deployment assessment.
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Developers
Check CUDA and framework support, inference libraries, model compatibility and availability in the cloud region you need. For a workload that does not depend on NVIDIA-specific tooling, test whether another accelerator can meet latency, throughput and portability requirements at lower total cost.
Gamers
NVIDIA’s AI infrastructure performance does not establish consumer GPU availability or pricing. Gaming remains a meaningful business, but it represented $16.0 billion of NVIDIA’s fiscal 2026 revenue, compared with $193.7 billion from Data Center.
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