Nvidia delivered a stellar fourth-quarter earnings report on February 26, 2025, crushing Wall Street expectations and demonstrating the chipmaker’s continued dominance in the AI hardware market. The company reported revenue of $39.33 billion, significantly exceeding analyst estimates of $38.25 billion and representing a remarkable 78% year-over-year growth.
The earnings report came amid heightened scrutiny following the emergence of Chinese AI company DeepSeek’s lower-cost AI model, which had sent shockwaves through Silicon Valley and raised questions about potential softening demand for Nvidia’s premium chips. However, Nvidia’s results decisively quashed those concerns.
Key Financial Highlights:
- Adjusted earnings per share: $0.89 vs. expectations of $0.84
- Data center revenue: $35.6 billion vs. estimates of $34.06 billion (93% year-over-year growth)
- Q1 2025 guidance: $43 billion expected revenue vs. analyst expectations of $41.78 billion
- Free cash flow: $15.52 billion, up 38% year-over-year
The star of the earnings report was Blackwell, Nvidia’s next-generation GPU platform. CFO Colette Kress told analysts that Blackwell demand “exceeded” expectations, with the product experiencing the company’s fastest ramp ever. CEO Jensen Huang characterized Blackwell demand as “extraordinary” and expressed confidence that the transition to Blackwell Ultra in the second half of 2025 would proceed more smoothly than the initial Blackwell rollout, which experienced a “hiccup” that cost the company “a couple months.”
Interestingly, Huang praised DeepSeek’s innovation, saying the Chinese model had “ignited global enthusiasm” and calling it “an excellent innovation” that open-sourced “a world-class reasoning AI model.” Kress noted that inference demand is accelerating, driven by test-time scaling and new reasoning models like OpenAI’s o3, DeepSeek R1, and Grok 3.
Challenges and Concerns: While the results were overwhelmingly positive, Nvidia faces some headwinds. The company’s gross margin is under pressure during Blackwell’s production ramp, currently in the low-70s percent range, though Kress expects it to improve to the mid-70s percent later in the fiscal year. Additionally, uncertainty around President Trump’s proposed tariffs loomed over the earnings call, with Kress calling the timing and amount “a little bit of an unknown.”
Nvidia’s stock experienced volatile after-hours trading, initially dipping 2% before recovering as positive commentary about Blackwell emerged. The stock had been down 2.2% year-to-date through Wednesday’s close, underperforming the S&P 500’s 1.3% gain.
Key Quotes
DeepSeek R1 has ignited global enthusiasm. It’s an excellent innovation, but even more importantly, it has open-sourced a world-class reasoning AI model, or chain of thought and reinforcement learning techniques.
CEO Jensen Huang praised DeepSeek’s contribution during the earnings call, demonstrating Nvidia’s confidence that innovation in AI models drives demand for their hardware rather than threatening it. This statement reframes the DeepSeek narrative from competitive threat to market catalyst.
Blackwell sales exceeded expectations… calling it the company’s fastest ramp ever for a product.
CFO Colette Kress highlighted Blackwell’s exceptional performance, signaling that Nvidia’s next-generation GPU platform is experiencing unprecedented demand. This statement directly addressed Wall Street’s concerns about potential demand softening and demonstrated the company’s execution capabilities.
The scale of post-training and model customization is massive and can collectively demand orders of magnitude more compute accelerating, driven by test time scaling and new reasoning models like OpenAI o3, DeepSeek R1, and Grok 3.
CFO Kress explained why inference demand is accelerating, highlighting a critical shift in AI workloads. This insight reveals that the computational requirements for deploying AI models may ultimately exceed training demands, creating sustained long-term growth opportunities for Nvidia.
Just because the chip is designed doesn’t mean it gets deployed. Our ability to deploy is lighting fast and create more advanced technology.
CEO Huang addressed competitive concerns by emphasizing that Nvidia’s advantage extends beyond chip design to include deployment speed and ecosystem integration. This highlights the company’s moat beyond pure hardware performance, including its CUDA software infrastructure.
Our Take
Nvidia’s earnings report represents a watershed moment for the AI industry, definitively answering whether the DeepSeek disruption would impact demand for premium AI hardware. The answer is a resounding no. What’s particularly striking is how Nvidia has reframed the DeepSeek narrative—rather than viewing efficient models as threats, Huang positioned them as catalysts that expand the AI market and drive more deployment.
The accelerating inference demand is perhaps the most significant revelation. While much attention has focused on training costs, the shift toward reasoning models and test-time scaling suggests that inference could become the dominant computational workload. This fundamentally changes the economics of AI deployment and ensures sustained demand for Nvidia’s hardware.
However, the gross margin pressure and tariff uncertainty represent real concerns that could impact profitability. Investors should monitor whether Nvidia can successfully navigate these challenges while maintaining its technological lead. The company’s confidence in the Blackwell Ultra transition suggests they’ve learned from earlier production hiccups, but execution risk remains. Overall, Nvidia continues to demonstrate why it remains the indispensable infrastructure provider for the AI revolution.
Why This Matters
Nvidia’s blockbuster earnings report carries profound implications for the AI industry and broader technology sector. The results definitively answer concerns that emerged after DeepSeek’s cost-efficient model suggested potential commoditization of AI infrastructure. Instead, Nvidia demonstrated that demand for cutting-edge AI hardware remains insatiable, with hyperscalers and enterprises continuing to invest billions in advanced computing resources.
The 93% year-over-year growth in data center revenue underscores that AI infrastructure spending shows no signs of slowing, even as the industry explores more efficient models. This validates the thesis that AI workloads—particularly training frontier models and scaling inference—require the kind of specialized, high-performance hardware that Nvidia dominates.
The accelerating inference demand highlighted by CFO Kress signals a critical shift in the AI market. As reasoning models and test-time scaling become more prevalent, the computational requirements are expanding exponentially, creating sustained demand for Nvidia’s products well beyond the initial training phase. This positions Nvidia to benefit from AI’s evolution from development to deployment at scale.
For businesses and investors, Nvidia’s performance reinforces that AI infrastructure remains a critical bottleneck and competitive advantage. Companies that can access and deploy advanced GPUs maintain significant advantages in developing and deploying AI applications, ensuring Nvidia’s central role in the AI ecosystem for the foreseeable future.
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