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To compare NVIDIA with other AI-chip stocks, look beyond reported AI revenue: first check what each company counts as AI-related, then compare growth, like-for-like profitability, cash generation, customer and product concentration, execution risks, export exposure, and valuation. NVIDIA, AMD, and Broadcom report different business categories and their latest located quarters ended on different dates, so their headline figures are not directly interchangeable.
What do NVIDIA, AMD, and Broadcom count as AI business?
Start with each company’s reporting boundary. NVIDIA reports a broad Data Center segment that includes data-center products and systems, including networking; AMD reports a Data Center segment; Broadcom’s AI semiconductor figure covers custom AI accelerators and AI networking. These are useful indicators of exposure, but they are not a matched measure of AI-chip sales, market share, total addressable market, or future demand.
| Company and period | Reported figure | What the figure covers | GAAP gross margin |
|---|---|---|---|
| NVIDIA, fiscal Q2 2027; quarter ended July 26, 2026 | $96.2 billion total revenue; $89.0 billion Data Center revenue | Data Center includes data-center products and systems, including networking; it is not a pure AI-chip measure. NVIDIA’s Q2 FY2027 results | 75.0% NVIDIA Q2 FY2027 10-Q |
| AMD, Q2 2026; quarter ended June 27, 2026 | Quarterly Data Center revenue: not stated in the cited Q2 filing excerpt. AMD Q2 2026 10-Q | AMD reports a Data Center segment; the cited excerpt does not give a matched AI-only revenue figure. | 54% AMD Q2 2026 10-Q |
| Broadcom, Q3 fiscal 2026; quarter ended August 2, 2026 | $16.7 billion AI semiconductor revenue, up 221% year over year; $29.6 billion consolidated revenue | AI semiconductor revenue is linked to custom AI accelerators and AI networking. Broadcom Q3 FY2026 results | Not stated in the cited Q3 results. |
The periods differ: NVIDIA’s quarter ended July 26, AMD’s June 27, and Broadcom’s August 2, 2026. AMD’s latest located gross-margin figure is therefore not for the same quarter as NVIDIA’s or Broadcom’s. A clean comparison should align fiscal periods as closely as possible and label any remaining mismatch.
How should you compare growth and profitability?
Use growth rates beside the period and the underlying driver, rather than treating a large percentage as a standalone ranking. Broadcom reported that its Q3 fiscal 2026 AI semiconductor revenue grew 221% year over year; that rate applies to its custom-accelerator-and-networking category, not to the same reporting boundary as NVIDIA’s Data Center segment.
#1 Best Overall
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
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For a longer view, NVIDIA’s fiscal 2026 annual report gives $215.9 billion in revenue, up 65% year over year. Within Data Center, compute revenue grew 59% and networking revenue grew 142% for that fiscal year. Those are full-year comparisons, not current-quarter growth rates, and should not be set beside a single-quarter result as if the periods matched. NVIDIA fiscal 2026 10-K
For profitability, compare GAAP with GAAP, or non-GAAP with non-GAAP, and separate gross margin from operating margin. The figures available here establish NVIDIA’s 75.0% GAAP gross margin for fiscal Q2 2027 and AMD’s 54% GAAP gross margin for Q2 2026, but the quarters are not aligned. The cited Broadcom Q3 results do not state a comparable gross margin, and this comparison does not supply a matched operating-margin series.
Rank #2
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
AMD said its Q2 2026 gross-margin increase reflected the absence of prior-year inventory and related charges associated with export controls on MI308, as well as favorable product mix, including higher Data Center revenue. That means the year-over-year change includes a comparison against prior charges; it should not be read as proof that the same margin improvement will recur. AMD Q2 2026 10-Q
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Keep fiscal-year and quarterly figures separate. For example, AMD’s fiscal 2025 annual filing reports $34.6 billion in net revenue and a 50% gross margin. Those full-year results do not match the period of NVIDIA’s or Broadcom’s quarterly figures. AMD 2025 10-K
Rank #3
- Built for Running LLMs Locally: RDNA 4, 128 AI Accelerators, up to 1,531 TOPS (INT4) for fast inference and fine-tuning
- 32GB GDDR6 VRAM for Large AI Models: 256-bit, up to 640GB/s bandwidth, run large language and multi-modal AI models without offloading
- Multi-GPU Scaling for Local AI Clusters: PCIe 5.0 and 2-slot design support dense multi-GPU builds for local AI training and inference clusters
- Diecast Shroud and Backplate: Wave-pattern design cuts memory temperature by up to 16%, keeping clocks steady during long AI training runs
- Phase-Change GPU Thermal Pad: Delivers superior thermal conductivity for consistent performance and longevity under heavy AI loads
What else determines whether reported demand becomes durable revenue?
Cash generation and investment
Review cash from operations, capital expenditures, free cash flow, working capital, and commitments over comparable periods. Revenue growth alone does not show how much cash a company retains or how much investment is required to support it. The disclosures cited here do not provide a fully matched cross-company set of those measures, so they cannot support a numeric cash-generation ranking.
Customers and product concentration
Read each company’s filing for major-customer disclosures, dependence on a small number of accelerator programs, and the breadth of workloads its products serve. The cited excerpts do not establish a comparable concentration ranking for NVIDIA, AMD, and Broadcom. Treat missing or differently presented disclosures as uncertainty, not evidence that a company has no concentration risk.
Rank #4
- 24GB GDDR7 ECC Memory: handles large AI, 3D and rendering files smoothly
- Powerful CUDA Compute - 8,960 CUDA cores for fast graphics and computing power
- AI & Ray Tracing Boost - Tensor of the 5th generation and RT cores of the 4th generation
- PCIe 5.0 x16 interface - fast data connection with modern systems
- 4 × DisplayPort 2.1 - Multi-monitor support for professional workflows
Execution and infrastructure constraints
AI-chip demand must move through fabrication, advanced packaging, high-bandwidth memory, customer delivery, and deployment. Data centers also require power, land, buildings, and capital. NVIDIA’s filing says constraints involving customers’ land, power, shell space, and capital can affect infrastructure buildout and NVIDIA’s financial performance. NVIDIA Q2 FY2027 10-Q
Export controls and geography
Policy can affect which products a company may sell, where it may sell them, and whether inventory needs to be written down. NVIDIA’s fiscal 2026 filing said it was effectively foreclosed from China’s data-center compute market at fiscal year-end; AMD reported MI308-related charges tied to U.S. export controls. These are company- and product-specific disclosures, not a fixed forecast of future policy or sales. NVIDIA fiscal 2026 10-K · AMD Q2 2026 10-Q
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you compare stock valuations fairly?
Operating growth does not establish whether a stock is cheap or expensive, and it does not predict investor returns. To compare valuation, use market capitalization or enterprise value from the same date, then pair each with the same forward horizon for earnings, sales, and free cash flow. Check whether estimates use comparable accounting definitions and ask what growth and margins the share price already assumes.
- Set one market date. Record share price, shares outstanding, and—if using enterprise value—cash and debt as of the same date for all companies.
- Choose a consistent estimate horizon. Compare the same next-twelve-month or fiscal-year period, not one company’s current year with another’s later year.
- Use multiple valuation measures. Compare forward price-to-earnings, enterprise value to sales, and enterprise value to free cash flow where the figures are meaningful and consistently defined.
- Test the assumptions. Consider how a slower growth rate, lower margins, delayed deployments, or higher investment needs would affect the valuation rather than assuming current momentum continues.
- Separate business quality from entry price. A strong business can still be priced for demanding results; a lower multiple can also reflect weaker expected growth or greater risk.
No synchronized share-price and consensus-estimate set is established by the cited disclosures, so they do not support a present-day valuation winner. Update prices and estimates together before drawing that conclusion.
Quick Recap
A practical comparison checklist
- Write down each company’s reporting definition for AI-related revenue before comparing amounts.
- Align quarter or fiscal-year dates and label every figure with its accounting basis.
- Compare year-over-year and sequential growth, then identify mix shifts, prior-period charges, and supply limitations that affect the rate.
- Compare GAAP gross and operating margins on matched periods; separately inspect cash flow and investment needs.
- Read customer, product, infrastructure, and export-control disclosures for company-specific risks.
- Use synchronized prices and forecast horizons for valuation, then evaluate which expectations are already reflected in each stock.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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