Nvidia Stock (NVDA): Is It Still a Buy in 2026?

By Jenna Lofton, StockHitter.com

What Nvidia Actually Does

Nvidia designs graphics processing units and the software ecosystem that runs on them. That description undersells it significantly.

Nvidia GPUs are widely used to train and run AI models, but not every major model was primarily trained on Nvidia hardware. Google says Gemini 1.0 was trained on its TPUs v4 and v5e; Anthropic describes using AWS Trainium, Google TPUs, and Nvidia GPUs. Hardware diversity is already part of the competitive picture.

But the hardware is only half the story. Nvidia’s CUDA toolkit combines libraries, a compiler, runtime, and development tools for GPU-accelerated computing. Its long-established software ecosystem helps explain why a purchasing decision involves much more than the chip’s specification sheet.

Nvidia’s Revenue Growth: The Numbers

Start with the latest report: Q2 fiscal 2027 results, released August 26, 2026, for the quarter ended July 26. Revenue was $96.2 billion. GAAP diluted EPS was $2.46; non-GAAP diluted EPS was $2.22. Those are different accounting measures, not interchangeable versions of the same valuation input. The reconciliation also excludes equity-security gains from adjusted earnings; strong headline profit is not identical to recurring operating cash generation.

For historical context, Q1 fiscal 2027, reported May 20, produced $81.6 billion in revenue, up 85 percent, and $1.87 non-GAAP diluted EPS. The 85 percent figure describes revenue growth, not earnings growth.

The original Q1 chart below remains a historical snapshot: Data Center contributed $75.2 billion, roughly 92 percent of sales. Q1 compute revenue was $60.4 billion and networking revenue was $14.8 billion. For Q2, total Data Center revenue reached $89.0 billion, also approximately 92 percent of company revenue.

For fiscal 2026 ended January 25, 2026, Nvidia generated $215.9 billion in revenue, up 65 percent. A useful accounting footnote: starting in Q1 fiscal 2027, Nvidia stopped excluding stock-based compensation from non-GAAP results and recast the comparisons it presents. Use that comparable series rather than mixing it with older adjusted EPS figures.

The CUDA Moat: Why Switching Is Hard

The most durable competitive advantage Nvidia holds is not its hardware. It is CUDA.

CUDA is the software platform that allows developers to use Nvidia GPUs for general-purpose computing. The accumulated code, tools, and developer familiarity can make an existing Nvidia installation easier to extend than to replace.

Switching away from CUDA is not simply a matter of buying different chips. It can mean adapting software libraries, retraining engineering teams, and validating performance and reliability. For a heavily optimized production system, that is a serious project. For a new workload built on portable frameworks, the hurdle may be lower.

That is the moat to watch: whether customers keep choosing Nvidia after comparing total deployment cost, software support, and performance on their own workloads. Vendor benchmark victories alone do not settle that question. A lovely slide is still not a production deployment.

Blackwell and Vera Rubin: What Has Changed

Blackwell succeeded Hopper and expanded Nvidia’s rack-scale approach, integrating Grace CPUs with GPU systems. It remains part of the installed-base story, but calling it the only current frontier while treating Rubin as a distant pipeline discussion is now out of date.

In its August 26 Q2 release, Nvidia said Vera Rubin was ramping into full production, with racks running at partners including CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure, and Nebius. That is a production-stage update, not merely a product-roadmap promise.

For investors, the important test is how that transition converts into shipped systems, recognized revenue, margins, and customer economics. Faster product cycles create upgrade opportunities, but they also bring execution risk and the possibility that customers wait for the next generation.

Hyperscaler Capex: A Demand Driver, Not a Floor

Nvidia benefits from infrastructure investment by large cloud providers and AI developers. But their total capital expenditure is not the same thing as orders for Nvidia: it also pays for buildings, power, networking, custom silicon, and other equipment.

At the March 16, 2026 GTC keynote, summarized in Nvidia’s March 19 recap, Huang said he saw at least $1 trillion in revenue across 2025 through 2027. That longer period should not be compared directly with an older $500 billion figure covering 2025–2026. Neither statement should be read as audited backlog or a guarantee.

A useful demand checklist is simpler than the trillion-dollar headlines: are customers deploying capacity, earning an adequate return on it, and continuing to fund the next build? Delays in power availability, financing, or AI monetization can slow supplier revenue even when the long-term technology case remains appealing. In Q2, operating cash flow was $24.1 billion versus $59.7 billion of GAAP net income, with receivables and inventory absorbing cash. That gap is worth monitoring alongside revenue growth.

For our full analysis of the hyperscaler capex cycle and its downstream beneficiaries, see our guide to best AI stocks to buy in 2026.

The Risk Case

I want to be direct about the risks because a complete analysis requires acknowledging them honestly.

Custom silicon is a significant long-term threat. The Google and Anthropic examples above show that alternative accelerators already handle important workloads. Customers can mix platforms rather than make one all-or-nothing choice, so Nvidia can grow in dollars while still facing pressure on share or pricing.

The counterargument is that Nvidia offers a broad, programmable platform with a substantial software ecosystem. That versatility can be valuable as models and workloads change. It does not make competing systems incapable of training frontier models, and it does not guarantee that Nvidia will win every inference workload.

Export restrictions are the second material risk. Nvidia recorded a $4.5 billion H20-related charge in Q1 fiscal 2026 for excess inventory and purchase obligations. Its Q3 fiscal 2027 outlook assumes no Data Center compute revenue from China; that is narrower than excluding all China Data Center revenue. Export policy remains a source of uncertainty.

Valuation is the third. Do not carry a June forward-P/E estimate into an October purchase decision. Recalculate price divided by the same provider’s expected EPS for a clearly identified fiscal period, then test a slower-growth case. A good company can still deliver a poor stock return if the entry price assumes too much.

Nvidia vs. AMD: The Competitive Reality

AMD is an important hardware and software competitor. Its July 23, 2026 announcement introduced Instinct MI400-series GPUs and Helios rack-scale systems, supported by ROCm software. An analysis that stops at MI300 misses the current product cycle.

The honest competitive picture is that customers evaluate complete systems: chips, networking, software, power consumption, availability, and engineering effort. Nvidia’s ecosystem is a substantial advantage, but AMD is also investing in a full stack rather than competing on chip price alone.

A hyperscaler evaluating GPU purchases is not just buying hardware performance per dollar. It is evaluating software compatibility, developer tooling, supply reliability, and switching costs. Compare results for the actual model and deployment configuration; do not treat either vendor’s best-case benchmark as a universal ranking.

Valuation Framework

The right starting point is a current price and a consistent earnings basis, not an old multiple repeated until it sounds permanent. Compare GAAP with GAAP, or adjusted with adjusted, and understand what each removes.

The relevant valuation question is whether the earnings trajectory justifies the price. Q1’s 85 percent revenue growth was not an earnings-growth rate: the company reported 140 percent comparable non-GAAP EPS growth and 214 percent GAAP EPS growth. Those unusually strong comparisons are historical results, not a sensible perpetual-growth assumption.

The framework I use is simple. Test several outcomes for revenue growth, margins, and the exit multiple. If customer spending slows, earnings estimates and the multiple can both fall. If growth stays strong, investors still need to ask how much of that success is already priced in. The spreadsheet needs a downside column, not just a party hat.

For readers comparing research tools, our Louis Navellier’s Growth Investor review discusses a paid service built around fundamental and quantitative stock screening. It is optional research, not evidence that NVDA is a bargain or a promise of superior returns. Check the publisher’s current subscription, renewal, and cancellation terms before paying.

Who Should Own Nvidia

For an investor who chooses to own it, Nvidia makes more sense as a risk-budgeted part of a diversified portfolio than as a substitute for diversification. An AI thesis is not an emergency fund.

A multi-year time horizon helps, but it does not eliminate the possibility of a large or lasting loss. Size the position so a sharp drawdown would not force a sale to meet ordinary expenses. There is no rule that the next correction must resolve upward just because earlier ones did.

Investors who need stability or near-term income should not assume an AI fund solves those needs either. Broader funds can reduce single-company concentration while still carrying substantial market and sector risk. For a framework on sizing growth positions, see our guide to investing strategies and our discussion of growth investing.