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

Updated: June 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.

The company’s GPUs are the dominant hardware platform for training and running artificial intelligence models. Every major AI model in production today, from OpenAI’s GPT series to Google’s Gemini to Meta’s Llama, was trained primarily on Nvidia hardware.

But the hardware is only half the story. Nvidia’s CUDA software platform, launched in 2007, created a parallel computing ecosystem that became the default environment for serious AI and scientific computing work. By the time the deep learning boom arrived in the 2010s, CUDA was already embedded in university curricula, enterprise workflows, and research pipelines worldwide. Competitors have never fully closed that gap.

Nvidia’s Revenue Growth: The Numbers

Nvidia reported Q1 fiscal 2027 earnings on May 20, 2026. Revenue hit $81.6 billion, up 85 percent year over year, beating Wall Street estimates of $78.8 billion. Adjusted EPS came in at $1.87, beating the $1.76 consensus.

Data center revenue reached $75.2 billion, accounting for 92 percent of total sales. That figure nearly doubled from a year earlier. Data center compute revenue hit $60.4 billion, up 77 percent year over year, while networking revenue reached $14.8 billion, up 199 percent year over year.

For the full fiscal year 2026 ended January 2026, Nvidia generated $215.9 billion in total revenue, up 65 percent from the prior year. The company has compounded revenue at a rate that no semiconductor business in history has matched at this scale.

The CUDA Moat: Why Competitors Keep Losing

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. Launched in 2007, it took nearly a decade to build critical mass. By the time AI demand exploded in the early 2020s, CUDA was already the default environment for AI development globally.

Switching away from CUDA is not simply a matter of buying different chips. It means rewriting software libraries, retraining engineering teams, migrating years of optimized code, and accepting a period of reduced performance and reliability. For most organizations running production AI workloads, that cost is economically irrational.

UBS analyst Timothy Arcuri noted in April 2026 that Nvidia’s ability to internally model, simulate, and benchmark alternative architectures is an underappreciated advantage. Nvidia can evaluate its competitors’ products faster and more accurately than those competitors can evaluate themselves. That intelligence advantage compounds over time.

The Blackwell Architecture: What It Is and Why It Matters

Blackwell is Nvidia’s current generation GPU architecture, succeeding the Hopper series. The B200 and GB200 GPUs deliver significantly improved performance per watt compared to Hopper, with rack-scale systems integrating Grace CPUs alongside GPU clusters.

CEO Jensen Huang described Blackwell sales as “off the charts” on the Q1 fiscal 2027 earnings call. Cloud GPUs based on Blackwell are sold out. Supply commitments for Nvidia hardware nearly doubled from $50.3 billion to $95.2 billion in the most recent quarter as the company locks in production capacity through calendar 2027.

The next architecture, Vera Rubin, is already in pipeline discussions with hyperscaler customers. Nvidia has committed to an annual architecture cadence, which means customers who want the most current hardware must remain on Nvidia’s upgrade cycle. That structure reinforces ecosystem lock-in with each generation.

Hyperscaler Capex: The Demand Floor

Nvidia’s revenue visibility rests on a foundation that most companies would trade anything to have: contracted infrastructure spending from the world’s largest technology companies.

Microsoft, Alphabet, Meta, and Amazon collectively guided over $600 billion in capital expenditure for 2026, with the majority earmarked for AI infrastructure. Wall Street analysts at Evercore and Bank of America estimate combined hyperscaler AI capex could exceed $1 trillion in 2027.

A significant portion of that spending flows directly into Nvidia hardware. Huang stated on the earnings call that Nvidia is the only platform running in every major cloud, powering every frontier AI model, and scaling from hyperscale data centers to the edge. That positioning is not marketing language. It reflects where the contracted purchase orders are sitting.

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 the most significant long-term threat. Google’s TPUs, Amazon’s Trainium chips, and Microsoft’s Maia accelerators are becoming more capable and more widely deployed. Custom silicon represented approximately 20.9 percent of the AI chip market in 2025 and is estimated to reach 27.8 percent in 2026. That share growth is real and it is accelerating.

The counterargument is that custom silicon is optimized for specific, narrow workloads. Nvidia’s GPUs remain the most flexible platform for general AI development, model training, and inference across diverse architectures. As long as AI model development continues at its current pace, the demand for flexible, programmable GPU compute remains structurally intact.

Export restrictions are the second material risk. Nvidia took a $4.5 billion charge in Q1 fiscal 2026 related to H20 chip export restrictions to China. The company has excluded China data center revenue from forward guidance. Any further escalation in export control policy represents a direct revenue headwind that cannot be diversified away.

Valuation is the third. At approximately 30 times forward earnings as of June 2026, Nvidia is priced for continued strong execution. A single quarter of significant earnings disappointment or guidance reduction would reprice the stock sharply. Investors buying today are paying for a future that has not yet been delivered.

Nvidia vs. AMD: The Competitive Reality

AMD is Nvidia’s most credible hardware competitor. The MI300 series accelerators have made real progress and AMD’s competitive pricing has become a differentiator for cost-sensitive workloads.

The honest competitive picture is this: AMD makes excellent chips that perform well in specific configurations. Nvidia makes the platform that the entire AI development ecosystem runs on. Those are different propositions.

A hyperscaler evaluating GPU purchases is not just buying hardware performance per dollar. It is evaluating software compatibility, developer tooling, supply reliability, and the cost of switching existing workloads. On every dimension beyond raw hardware price, Nvidia maintains a substantial lead. AMD’s path to meaningful market share gains runs through CUDA compatibility, and CUDA compatibility is Nvidia’s to control.

Valuation Framework

Nvidia is not cheap by any traditional valuation metric. At approximately 30 times forward earnings, it trades at a premium to the S&P 500 and to most semiconductor peers.

The relevant valuation question is not whether the multiple is high. It is whether the earnings growth trajectory justifies it. A business growing earnings at 85 percent year over year with 75 percent gross margins and demand visibility through contracted hyperscaler capex is not a typical semiconductor business. It earns a premium multiple.

The framework I use is simple. If hyperscaler AI capex continues to grow at or near current rates through 2027, Nvidia’s earnings will likely grow into its current multiple and then some. If capex growth decelerates significantly, the multiple compresses alongside the earnings revision. That is the binary the stock is pricing.

For investors who want a quantitative earnings growth screening framework applied to Nvidia and AI infrastructure peers, Louis Navellier’s Growth Investor uses a proprietary quantitative model that has historically identified accelerating earnings growth names early. His methodology is specifically designed for the kind of high-growth, high-multiple businesses that define the current AI infrastructure cycle.

Who Should Own Nvidia

Nvidia belongs in the satellite portion of a well-constructed portfolio, not the core. It is a high-conviction growth position, not a defensive holding.

Investors with a multi-year time horizon and the psychological capacity to hold through corrections of 20 to 30 percent are the right owners of NVDA. Those corrections have happened multiple times in the past three years. Each one felt like something worse than it turned out to be. Each one resolved upward.

Investors who need stability, income, or cannot tolerate significant short-term volatility should express AI infrastructure exposure through broader vehicles rather than Nvidia directly. For a framework on how to size high-conviction growth positions within a broader portfolio, see our guide to investing strategies and our full discussion of growth investing.