There is no universal Nvidia-versus-AMD winner. For a gaming or workstation purchase, the better choice depends on the exact cards, price, workload, resolution, power limit, memory, software and availability. For data centers, Nvidia currently reports much greater segment scale, while AMD is growing rapidly with EPYC processors and Instinct accelerators. For investors, those growth rates must be weighed against supply, competition, export controls, customer concentration, capital requirements and product-transition risks rather than treated as a buy signal.
The short answer
- Consumer GPUs: compare named models under the workload you actually run. Gaming rasterization, ray tracing, creator applications and AI tools can produce different rankings. Company-level reputation cannot replace matched tests.
- Data centers: Nvidia reported $89.0 billion of Data Center revenue in fiscal Q2 2027, compared with AMD’s $6.718 billion in the quarter ended June 27, 2026. Those quarters end about a month apart, and the figures are not a same-period market-share calculation.
- Investing: Nvidia’s disclosures emphasize supply constraints, system transitions, infrastructure requirements and customer concentration. AMD emphasizes Nvidia’s market position and software ecosystem, semiconductor cyclicality, rapid product change and demand volatility. Neither company’s segment growth alone establishes which stock is the better investment.
What the companies actually report
The latest reported periods are not aligned, so put the dates beside every number. Nvidia’s fiscal Q2 2027 ended July 26, 2026. AMD’s latest located quarter ended June 27, 2026.
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| Company and period | Total revenue | Data Center revenue | Year-over-year Data Center growth | Management-stated growth drivers |
|---|---|---|---|---|
| Nvidia fiscal Q2 2027, quarter ended July 26, 2026 | $96.2 billion | $89.0 billion | 117% | Blackwell Ultra infrastructure and accelerated-computing and AI products |
| AMD quarter ended June 27, 2026 | $11.536 billion | $6.718 billion | 107% | Demand for fifth-generation EPYC processors and Instinct MI350 Series GPUs |
| AMD fiscal 2025 | Data Center figure reported separately | $16.6 billion | 32% increase from 2024 | Demand for fifth-generation EPYC processors and MI350 Series GPUs |
These are company-reported segment figures, not independent market-share estimates and not GPU-only revenue. Nvidia’s Data Center category includes a broad platform of accelerators, CPUs, networking, systems and related software. AMD’s Data Center segment includes EPYC server processors and Instinct accelerators. Because the definitions, fiscal calendars and reporting periods differ, dividing one company’s number by the other’s does not produce a valid market-share calculation.
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How to compare consumer and workstation GPUs
Start with an exact model, not the logo
Nvidia’s roots are in PC graphics, and its annual filing describes the same GPU architecture being used across gaming and other computing fields. AMD reports Radeon gaming GPU activity. Those descriptions establish the scope of each business, but they do not prove that every product generation, price tier or application favors one company.
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Write down the two specific cards you are considering, including the board partner, memory configuration and current street price. A comparison such as “Nvidia versus AMD” is too broad to answer a purchase question. A comparison such as “Card A against Card B at 1440p with a 750-watt power supply” can be tested.
Match the card to the workload
- Rasterized gaming: compare average frame rate and frame-time consistency at the resolution and quality preset you use. Check demanding games separately from lightweight esports titles.
- Ray tracing: use tests with ray tracing enabled, because the ranking can differ substantially from conventional rasterized rendering.
- Creator applications: verify support for your video editor, 3D renderer, color workflow and plug-ins. A card that is fast in games may not have the same acceleration or stability in a particular production application.
- Local AI and compute: check whether the frameworks, model formats and acceleration libraries you need support the card. Memory capacity can determine whether a model runs at all, independent of headline speed.
No matched independent card benchmarks are established here, so a universal performance winner would be an unsupported conclusion. Use current tests for the exact models and software versions instead of extrapolating from the companies’ financial filings.
Compare memory, bandwidth and power together
Memory capacity is a practical limit for high-resolution textures, large video projects and local AI models. Memory bandwidth affects how quickly data can be fed to the GPU, but it is not a substitute for testing the complete card. Record capacity and bandwidth for both cards, then check the application’s actual requirements.
Power affects more than the electricity bill. Confirm the card’s board-power recommendation, the power supply’s capacity and connector requirements, case clearance, cooling and expected fan noise. Performance-per-watt is most useful when measured under the same workload and frame-rate target; comparing a laboratory peak to a different test condition can mislead.
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Check software and operating-system compatibility before buying
List the operating system, game engines, rendering APIs, creator applications, plug-ins and AI frameworks you intend to use. Check the developer’s supported hardware list and the driver branch required by your application. Proprietary features, open standards, encoder support and the maturity of a particular software stack can matter more than a small difference in a synthetic benchmark.
For a workstation, also check certification for the applications used by your team, multi-monitor requirements, virtualization support and the vendor’s driver-release policy. A card that works for gaming may still be unsuitable for a production workflow that requires a certified driver.
Use price and availability at the time of purchase
Compare the actual delivered price, warranty, return policy and stock status on the day you buy. Launch prices and recommended prices can diverge from retail prices, especially when supply is tight. Include the cost of a larger power supply, additional cooling or a case upgrade if the replacement card needs them.
Do not use a consumer-card listing as evidence of data-center performance or investment merit. Consumer availability is a purchasing variable; it is not a proxy for a company’s server-platform revenue.
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A practical consumer-GPU worksheet
| Criterion | Questions to answer for each named card |
|---|---|
| Target workload | Which games, renderers, editors or AI tools will run most often? |
| Resolution and quality | Is the target 1080p, 1440p or 4K, and which quality or ray-tracing settings matter? |
| Performance evidence | Are the tests matched for model, driver, game version, resolution and settings? |
| Memory | Does capacity and bandwidth meet the largest project or model requirement? |
| Power and thermals | Can the existing supply, connectors, case and cooling handle the card? |
| Software | Are required APIs, applications, plug-ins and AI frameworks supported? |
| Ownership cost | What are the delivered price, warranty, noise, energy use and upgrade costs? |
| Availability | Can you buy the exact configuration with acceptable return terms now? |
How Nvidia and AMD’s data-center businesses differ
Nvidia: a very large, integrated Data Center segment
Nvidia’s latest filing reports $89.0 billion in Data Center revenue on $96.2 billion of company revenue for fiscal Q2 2027, with Data Center revenue up 117% year over year. Nvidia says the increase was driven by data-center products for accelerated computing and AI solutions, and specifically identifies Blackwell Ultra infrastructure as a growth driver.
Nvidia reports two operating segments: Compute & Networking and Graphics. Its data-center proposition extends beyond an accelerator chip to include server CPUs, networking, complete systems, software and deployment support. That breadth matters because customers building large AI clusters must make the components work as one system.
Nvidia says Blackwell remained the majority of its system shipments in fiscal Q2 2027. It also states that production shipments of its next-generation Vera Rubin architecture began in fiscal Q3 2027. The same filing describes supply constraints, so reported demand should be read alongside the company’s ability to manufacture, assemble and deliver complete systems.
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AMD reported $6.718 billion of Data Center revenue for the quarter ended June 27, 2026, up 107% year over year. AMD attributes the increase primarily to strong demand for AMD EPYC processors and AMD Instinct MI350 Series GPUs. Its 2025 annual report reported $16.6 billion of Data Center revenue, up 32% from 2024.
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AMD’s Data Center reporting combines server CPUs and accelerators. That makes the segment useful for understanding the company’s server business, but it prevents a clean comparison with a hypothetical GPU-only number. AMD’s annual report also describes networking products and previewed its Helios rack-scale platform, showing an effort to address more of the system rather than selling only an individual processor.
Why the revenue gap is not a complete platform score
Nvidia’s reported Data Center revenue is currently much larger than AMD’s reported Data Center revenue, while both companies report rapid year-over-year growth. The gap does not by itself tell you which accelerator is faster for a particular model, which platform has the lower total cost of ownership or how much revenue comes from GPUs versus CPUs, networking and systems.
For a procurement decision, evaluate the whole deployment:
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →| Deployment question | What to measure |
|---|---|
| Workload performance | Throughput, latency, utilization and scaling for your models or applications |
| System economics | Accelerator, server, networking, software, support and facility costs |
| Power and cooling | Rack power, cooling capacity, electricity cost and density limits |
| Software porting | Existing code changes, library coverage, compiler support and developer effort |
| Networking and scale | Interconnect performance, topology, storage access and multi-node behavior |
| Supply and deployment | Allocation, lead time, integration capacity and replacement availability |
| Support | Enterprise support terms, driver cadence, security updates and escalation paths |
Run a representative pilot when the purchase is material. Measure the complete system under production-like batch sizes and failure conditions rather than selecting a platform from a single chip specification.
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Investment risks to compare
Nvidia’s disclosed risks
- Supply constraints: Nvidia reports constraints affecting its ability to meet demand. It also discusses commitments to secure future supply, which can create obligations before customer deployments are complete.
- Product-transition complexity: Moving from Blackwell systems toward Vera Rubin requires new production, integration and customer qualification work. A transition can affect timing, costs and inventory even when demand remains strong.
- Infrastructure dependence: AI systems need sufficient data-center capacity, electricity, cooling, networking and capital. Nvidia’s annual filing warns that shortages in data-center, energy or capital resources could delay customer buildouts and affect Nvidia’s performance.
- Customer concentration: A small number of very large buyers can account for a significant share of orders. Changes in their capital plans, internal chips or deployment schedules can move quarterly results.
- Export restrictions: Trade rules can limit which products Nvidia may ship to particular markets and can require redesigned products or additional compliance costs. Nvidia’s latest quarterly report says restricted H200 shipments to China were less than 1% of Data Center revenue in that quarter; that is a period-specific disclosure, not a guarantee about future quarters.
AMD’s disclosed risks
- Competitive position and software ecosystem: AMD’s latest filing says a competitor’s data-center GPU market position, financial resources and proprietary software ecosystem can influence customers and limit alternatives. Porting software and validating a new platform can be a greater barrier than hardware specifications suggest.
- Semiconductor cyclicality: Demand, pricing, inventory and capacity can swing through the semiconductor cycle. A strong quarter does not ensure that growth will continue at the same rate.
- Rapid product change: Frequent CPU, GPU, networking and platform updates can shorten product windows and require continuing engineering and customer-qualification spending.
- Supply-demand volatility: AMD identifies swings in supply and demand, including historical volatility in cryptocurrency-related GPU demand, as a risk to revenue and inventory planning.
- Export-control charges: AMD’s 2025 filing recorded approximately $440 million in net inventory and related charges associated with U.S. export controls on MI308 products. This illustrates how regulation can affect reported results even when customer demand exists.
Risks shared by both companies
Both companies face export restrictions, customer concentration, manufacturing dependencies and the possibility that customer spending shifts faster than supply chains can respond. Both also depend on customers having the money, power and physical capacity to deploy large systems. A backlog or announced product is not the same thing as recognized revenue.
Read the risk factors together with segment mix, gross margin, operating expenses, inventory, receivables, capital commitments and cash flow. A revenue-growth percentage can rise because of a small comparison base, a product launch or a temporary supply release; it does not reveal durability on its own.
A disciplined way to compare the companies as investments
- Align the periods. Put Nvidia’s July 26, 2026 quarter and AMD’s June 27, 2026 quarter on separate lines. Do not label the comparison a same-quarter market-share analysis.
- Separate segments. Compare Nvidia Data Center with AMD Data Center, and keep gaming, client, graphics and other businesses out of the calculation unless you are deliberately analyzing company-wide mix.
- Identify what the revenue contains. Nvidia’s Data Center and AMD’s Data Center are platform segments, not consistently isolated accelerator businesses.
- Test growth quality. Examine product mix, customer concentration, pricing, supply limits, inventory and whether growth reflects repeat deployments or a one-time transition.
- Map the competitive moat. Look at software compatibility, developer effort, networking, system integration, support and switching costs, not only compute specifications.
- Stress the risks. Model slower customer capex, export restrictions, a delayed product transition, higher component costs and weaker utilization.
- Use valuation separately. The supplied filings establish revenue and disclosed risks, but not current valuation multiples, earnings forecasts or a fair value. Obtain current market data before making an investment decision.
This process helps distinguish a business comparison from a stock recommendation. It also prevents a spectacular quarterly growth rate from being mistaken for a complete investment thesis.
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Choosing the right comparison for your question
If you are buying a graphics card
Choose two currently available models, define the games or applications, set the target resolution and frame-rate, then compare matched tests, memory, power, software and total delivered cost. Recheck stock and driver support immediately before purchase.
If you are planning an AI or server deployment
Benchmark your own workload or a close representative, include CPUs, accelerators, networking and storage, and price the facility requirements. Confirm software-porting effort, support terms, supply commitments and deployment time. Segment revenue is useful business context, not a substitute for an architecture evaluation.
If you are evaluating the stocks
Use the latest filings for period-aligned revenue, segment definitions and risk disclosures. Then add current valuation, balance-sheet analysis, cash-flow trends, customer concentration details and your own assumptions about demand and regulation. Do not buy either stock solely because its latest Data Center growth percentage is higher.
Bottom line
Nvidia currently reports a far larger Data Center business and a 117% year-over-year increase in fiscal Q2 2027 revenue for that segment, while AMD reports a smaller but rapidly growing Data Center business driven by EPYC and Instinct products. That establishes scale and reported momentum, not a universal hardware or investment winner. For GPUs, decide at the model-and-workload level. For data centers, compare complete platforms and deployment economics. For investing, balance growth against supply, software, infrastructure, export and concentration risks, and refresh the figures when newer filings or products become available.
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