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There is no universal Nvidia-versus-AMD winner. A gaming-card choice depends on the exact models, workload, price, power limit, memory and software you need. A data-center comparison must include CPUs, accelerators, networking, systems, software, supply and deployment capacity. An investment comparison is different again: reported growth, concentration, export exposure and product-transition risk matter more than choosing the faster gaming card.
The latest company filings illustrate why the comparisons must stay separate. NVIDIA reported $96.2 billion in total revenue and $89.0 billion in Data Center revenue for its fiscal second quarter ended July 26, 2026, with Data Center revenue up 117% year over year. AMD reported $11.536 billion in total revenue and $6.718 billion in Data Center revenue for the quarter ended June 27, 2026, up 107% year over year. Those quarters end about a month apart, and the segment figures are not a same-period market-share calculation or a measure of GPU-only revenue.
What the latest filings actually show
Start with the reporting periods before drawing conclusions. NVIDIA’s fiscal Q2 2027 ended July 26, 2026. AMD’s latest located quarter ended June 27, 2026. The dates, fiscal calendars and segment definitions differ, so a percentage comparison shows each company’s reported year-over-year change, not which company owns a particular share of the accelerator market.
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|---|---|---|---|
| Reporting period | Fiscal Q2 2027, quarter ended July 26, 2026 | Quarter ended June 27, 2026 | Not the same quarter; do not treat as a synchronized market-share snapshot. |
| Total revenue | $96.2 billion | $11.536 billion | Company-reported consolidated revenue for each respective period. |
| Data Center revenue | $89.0 billion | $6.718 billion | Segment revenue, not accelerator-only revenue. |
| Year-over-year Data Center growth | 117% | 107% | Reported growth rates for different periods and different revenue bases. |
| Stated growth drivers | Blackwell Ultra infrastructure and accelerated-computing and AI products | EPYC processors and Instinct MI350 Series GPUs | Management explanations in the respective filings, not independent market-share evidence. |
NVIDIA said Blackwell remained the majority of its system shipments in that quarter and that production shipments of its next-generation Vera Rubin architecture began in fiscal Q3 2027. The same filing described supply constraints. AMD’s 2025 annual report put its full-year Data Center revenue at $16.6 billion, up 32% from 2024, citing fifth-generation EPYC processors and MI350 Series GPUs. These figures show strong reported growth at both companies, but they do not establish equal scale or hardware efficiency.
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How to compare consumer and workstation GPUs
Compare named products, not company reputations. A high-end card from one generation can lose to a newer midrange card from the other company, while a card that wins in rasterized games may be less suitable for ray tracing, video production or machine-learning software. The filings describe NVIDIA’s long-standing GPU business in PC graphics and AMD’s Radeon activity, but they contain no matched independent benchmarks. Do not infer a universal performance winner from corporate revenue growth.
1. Define the workload before looking at a card
- Gaming rasterization: Identify the games, target resolution and refresh rate. A 1080p esports system, a 1440p high-refresh build and a 4K single-player build can favor different cards.
- Ray tracing: Record whether your games use ray-traced lighting and whether you are willing to enable upscaling or frame-generation features. Compare results with those features measured in the same mode on both cards.
- Creator applications: List the specific editor, renderer or encoder. Plugin support, export formats and timeline behavior can matter more than a synthetic score.
- Compute and AI: Check the exact framework, supported operators, driver version and memory requirement. A card that has adequate raw throughput can still be unsuitable if the application cannot use its software stack.
- Workstation reliability: For professional use, include certified applications, driver support policy, error-correction features where offered and vendor support terms.
2. Compare exact models at the price you can actually buy
Write down the full model name, board power, memory capacity, memory bandwidth, cooler design, warranty and current street price. Use results from reviews that test the same game or application, settings and resolution. A launch price, a temporary promotion and an out-of-stock listing are not interchangeable values. The cited company filings do not state current consumer-card prices, stock levels or matched frame-rate results, so those fields must be verified for the models available when you purchase.
| Consumer comparison axis | What to record for a GeForce RTX card | What to record for a Radeon card |
|---|---|---|
| Performance | Model-specific frame rates or application throughput under identical settings; not stated in the cited filings | Model-specific frame rates or application throughput under identical settings; not stated in the cited filings |
| Memory | Capacity and bandwidth for the exact board; not stated in the cited filings | Capacity and bandwidth for the exact board; not stated in the cited filings |
| Power and thermals | Board power, connector, cooler clearance and measured noise; not stated in the cited filings | Board power, connector, cooler clearance and measured noise; not stated in the cited filings |
| Software | Required APIs, drivers, creator tools and AI frameworks for your applications; card-specific support is not stated in the cited filings | Required APIs, drivers, creator tools and AI frameworks for your applications; card-specific support is not stated in the cited filings |
| Availability | Current retailer stock and return terms; not stated in the cited filings | Current retailer stock and return terms; not stated in the cited filings |
3. Check the rest of the system
Confirm the power supply has the required capacity and connectors, the case has enough length and thickness clearance, and the motherboard and operating system support the card. Small-form-factor systems may be limited by heat and noise even when a card fits physically. A workstation that runs long renders should be evaluated at sustained power, not only at a short benchmark peak.
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4. Measure value for your own workload
Calculate cost per delivered frame, render or inference result using a test that resembles your work. Include electricity if the machine runs for many hours, but keep the assumption visible: energy prices and usage vary. If two cards are close in performance, memory headroom, software compatibility, noise and warranty can be more important than a small benchmark difference.
5. Treat software compatibility as a first-class requirement
Check the application’s current documentation and the driver version it supports. Verify plug-ins, hardware encoders, virtual-production tools and machine-learning packages before buying. AMD’s filings discuss competitive pressure from a rival’s proprietary software ecosystem; that disclosure is a reminder that software lock-in and porting effort can affect the practical value of a GPU. It is not evidence that every application favors one vendor.
How the data-center businesses differ
A data-center platform is more than an accelerator card. Buyers evaluate host CPUs, accelerators, networking, storage, rack systems, orchestration software, support, power and cooling, supply commitments and the time required to put a production cluster into service.
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NVIDIA: an accelerator-led, integrated platform
NVIDIA reports two operating segments, Compute & Networking and Graphics. In its fiscal Q2 2027 filing, it said revenue growth was driven by data-center products for accelerated computing and AI solutions. The company attributed the quarter’s Data Center performance to Blackwell Ultra infrastructure and said Blackwell was still the majority of system shipments. It also reported that production shipments of the Vera Rubin architecture began in fiscal Q3 2027.
That roadmap creates both an opportunity and a planning issue. A customer may want the newest architecture, but a transition can affect qualification, rack design, software validation, delivery schedules and resale value of equipment purchased immediately before the change. NVIDIA also disclosed supply constraints, so a paper specification is not the same as deployable capacity.
AMD: EPYC CPUs and Instinct accelerators in the Data Center segment
AMD’s Data Center segment includes EPYC server processors and Instinct accelerators. For the quarter ended June 27, 2026, AMD reported $6.718 billion of Data Center revenue and attributed the increase primarily to strong demand for EPYC processors and Instinct MI350 Series GPUs. Its 2025 annual report reported $16.6 billion of full-year Data Center revenue, 32% higher than in 2024. AMD also described networking products and previewed its Helios rack-scale platform.
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Those disclosures show a broader server strategy than a GPU-only comparison. EPYC capacity, Instinct accelerator availability, networking and a rack-scale design can be evaluated as one deployment, but the filings do not provide a matched, independent benchmark against a specific NVIDIA system.
| Data-center question | NVIDIA evidence | AMD evidence | What a buyer still has to verify |
|---|---|---|---|
| Accelerators | Blackwell Ultra drove reported growth; Vera Rubin production shipments began in fiscal Q3 2027 | Instinct MI350 Series demand drove reported growth | Workload throughput, memory behavior, software porting and availability for the exact configuration. |
| Server CPUs | CPU contribution is not separately stated in the cited quarter’s Data Center figure | EPYC processors are a stated Data Center growth driver | Host-CPU performance, platform compatibility and total system cost. |
| Networking and systems | Compute & Networking segment and system shipments are part of the disclosed business | Networking products and the previewed Helios rack-scale platform are described in the annual report | Topology, bandwidth, latency, management tools, rack integration and support. |
| Software and support | Integrated accelerated-computing and AI platform; exact customer software results are not stated in the filings | AMD identifies a rival’s proprietary software ecosystem as a competitive factor | Porting effort, supported frameworks, service-level terms and internal expertise. |
| Supply and deployment | Supply constraints disclosed; Blackwell-to-Vera Rubin transition underway | Product availability and deployment timing are not stated in the cited filings | Allocation, lead time, power delivery, cooling and commissioning schedule. |
| Reported Data Center scale | $89.0 billion in fiscal Q2 2027 | $6.718 billion in the quarter ended June 27, 2026 | Periods differ by about a month; figures are not GPU-only market share. |
Do not compare NVIDIA Data Center revenue with AMD’s Gaming revenue or with AMD’s combined Client and Gaming results. Use the Data Center segment on both sides, preserve the quarter-end dates and remember that neither segment isolates accelerator revenue consistently.
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A practical data-center buying sequence
- Classify the workload: Separate training, inference, scientific computing, database analytics and general virtualization. Record model size, precision, batch size, latency target and utilization.
- Specify the complete system: Include accelerators, host CPUs, memory, storage, networking, rack design and management software rather than comparing a single chip.
- Model total cost: Add purchase price, licensing, power, cooling, facility upgrades, support and staff time. Ask vendors for power and performance data for the exact workload.
- Validate software migration: Inventory frameworks, kernels, libraries and monitoring tools. Estimate engineering time to port and maintain them, not just the initial installation.
- Confirm capacity and delivery: Get written lead times, allocation assumptions, replacement terms and a plan for shortages or architecture transitions.
- Pilot before scaling: Run a representative workload, measure throughput per watt and per rack, test failure recovery and verify that production telemetry works.
Investment risks: what differs and what overlaps
Segment growth is an important fact, not a complete investment thesis. A durable assessment combines revenue mix, margins and cash generation from the filings with customer concentration, supply commitments, export rules, product transitions, capital requirements and competitive alternatives. The following risks are disclosures by the companies, not forecasts that a particular outcome will occur.
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NVIDIA-specific risks
- Supply and commitments: NVIDIA disclosed supply constraints and commitments to secure future supply. Shortfalls in advanced packaging, memory, networking or complete systems could limit shipments even when demand is strong.
- Architecture transitions: Moving from Blackwell systems toward Vera Rubin can create qualification, inventory, scheduling and support complexity. The cost and timing of a transition can affect customers and NVIDIA’s results.
- Energy and facility capacity: Large AI deployments require available power, cooling, networking and capital. NVIDIA’s annual filing warned that shortages in data-center, energy or capital resources could delay customer buildouts and affect performance.
- Customer concentration: A small number of very large customers can make quarterly results sensitive to their capital plans, deployment timing and bargaining power.
- Export restrictions: Rules governing advanced products can limit eligible markets or require redesigned products. NVIDIA’s latest quarterly filing said restricted H200 shipments to China were less than 1% of Data Center revenue in that quarter; that is a period-specific disclosure, not a permanent estimate.
AMD-specific risks
- Competitive position and software: AMD’s latest filing says a competitor’s Data Center GPU position, financial resources and proprietary software ecosystem can influence customers and limit alternatives. Winning hardware specifications alone may not overcome switching and porting costs.
- Semiconductor cyclicality: AMD identifies swings in supply and demand, rapid product changes and industry cycles as risks. Inventory can build when a product transition or demand forecast changes.
- GPU-demand volatility: The company also notes that cryptocurrency-related GPU demand can fluctuate, which can distort channel inventories and comparisons across periods.
- Export controls and charges: AMD’s 2025 annual report recorded approximately $440 million in net inventory and related charges associated with U.S. export controls on MI308 products. The charge demonstrates financial exposure to policy changes; it does not predict a future charge.
- Customer concentration and supply: As with NVIDIA, large customers, foundry capacity, packaging, memory and system partners can affect shipment timing and reported growth. Exact concentration and lead-time figures should be taken from the latest filing available at the time of analysis.
Risks shared by both companies
| Risk area | Why it matters | Questions to ask in the next filing |
|---|---|---|
| Customer concentration | A few buyers can move revenue sharply when orders are accelerated, delayed or renegotiated. | Has the largest-customer mix changed? Are commitments cancellable or conditional? |
| Export policy | Restrictions can remove markets, force product redesigns or create inventory charges. | Which products and regions are affected, and are charges or reserves disclosed? |
| Supply chain | Demand cannot become revenue without wafers, packaging, memory, networking and complete-system capacity. | Are constraints easing, moving to another component or being secured through commitments? |
| Infrastructure bottlenecks | Power, cooling, buildings and customer capital can delay deployments. | Are orders converting to installations, and are customers signaling longer deployment times? |
| Product transitions | New architectures can increase performance while stranding inventory or requiring requalification. | What is shipping now, what is in transition and how are customers supported? |
| Competitive alternatives | Customers can change suppliers when performance, software, price or availability changes. | Are design wins broadening, and is software adoption reducing or increasing switching friction? |
A disciplined way to evaluate the companies as investments
This framework is for organizing public information, not for telling you which stock to buy.
- Normalize the periods: Put each company’s quarter-end date beside every revenue and growth figure. Do not combine NVIDIA’s July 26, 2026 quarter with AMD’s June 27, 2026 quarter as if they were simultaneous.
- Separate segments: Compare Data Center with Data Center. Keep gaming, client, graphics and other businesses visible so a fast-growing segment does not hide weakness elsewhere.
- Separate revenue from market share: Segment revenue includes products and services defined by each company. It is not a shipment count, accelerator-only figure or independent market-share estimate.
- Test growth quality: Examine whether growth comes from volume, price, mix, a new architecture, a customer ramp or a one-time comparison. Read management’s explanation, then check cash conversion, inventory and receivables in the same filing.
- Stress supply and transition assumptions: Model a delayed product launch, constrained packaging, higher memory cost or a customer deployment pushed into a later quarter. Compare the effect on revenue, margins and working capital.
- Stress policy exposure: Consider a tighter export rule, a restricted product family or an inventory charge. Use disclosed exposures rather than assigning a probability you cannot support.
- Assess the platform: For each company, map accelerators, CPUs, networking, systems, software, support and partner capacity. A chip-level advantage may not translate into a lower-cost production cluster.
- Demand a valuation context: Growth percentages alone do not tell you whether a security is attractively priced. Use current filings and market data for valuation, dilution, cash, debt and expected capital needs, and keep forecasts clearly labeled as assumptions.
What to monitor when new information arrives
- Data Center revenue and mix, with the quarter-end date and definition of the segment.
- Shipment and qualification progress for Blackwell Ultra, Vera Rubin, MI350 and later products.
- Evidence that supply constraints are easing or shifting to memory, packaging, networking, power or cooling.
- EPYC and Instinct demand, system-level deployments and the status of AMD’s rack-scale plans.
- Customer concentration, contractual commitments and signs that large deployments are being delayed.
- Export-control changes, restricted-product revenue and any inventory or related charges.
- Software support, porting requirements and customer adoption of each platform’s development stack.
- Consumer-card pricing, stock, power requirements and independent benchmarks for the exact models under consideration.
Bottom line for a buyer or analyst
Choose a consumer GPU by matching a specific model to a specific workload and system, then verify current price, benchmarks, memory, power and software support. Choose a data-center platform by evaluating the whole rack and the deployment path, not just accelerator specifications. Evaluate the companies as investments by normalizing their different reporting periods and weighing growth against supply, transitions, infrastructure, customer concentration, competition and export exposure. The available filings support strong, rapidly growing Data Center businesses at both companies, but they do not identify a universal gaming winner, establish accelerator market share or justify a stock decision on growth alone.
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