In the first quarter of Nvidia's 2026 fiscal year, the company's revenue rose to ***** billion U.S. dollars. Nvidia's data center revenue, a segment that includes technologies that are being deployed for accelerated computing and generative AI applications, generated ***** billion U.S. dollars. This is a major jump from the ***** billion U.S. dollars of data center revenue posted in the same quarter of the previous fiscal year.

In the first quarter of Nvidia's 2026 fiscal year, revenue from data centers amounted to ***** billion U.S. dollars. This is a dramatic increase from the ***** billion U.S. dollars the company generated in this segment in the same quarter of the previous fiscal year. Nvidia’s technologies are being deployed for accelerated computing and generative AI applications, notably ChatGPT. Nvidia’s move beyond gaming and into AI Nvidia’s solutions are being used to train and run various large language models, most notably the one developed by OpenAI. ChatGPT – which generates human-like responses to user queries within seconds – was trained using tens of thousands of Nvidia graphics processing units (GPUs), linked together in an AI supercomputer belonging to Microsoft. Nvidia’s competitors in the AI space include cloud providers Nvidia’s earnings have helped to strengthen the company’s position in the exclusive tech three trillion club, a ranking of companies based on market capitalization, putting Nvidia up alongside the likes of Apple and Microsoft. While fellow chipmakers AMD and Intel may seem the natural competitors to Nvidia’s AI crown, the major hyperscalers also pose a substantial threat to Nvidia going forward.

Nvidia reported $0.96 in EPS Earnings Per Share for its fiscal quarter ending in April of 2025. Data for Nvidia | NVDA - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Nvidia reported $44.1B in Sales Revenues for its fiscal quarter ending in April of 2025. Data for Nvidia | NVDA - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last August in 2025.

In its 2025 fiscal year, Nvidia's revenue in the United States amounted to ***** billion U.S. dollars, a substantial jump from the ***** billion U.S. dollars seen in the previous fiscal year. Revenue in Taiwan amounted to ***** million U.S. dollars in the 2025 fiscal year, while China related revenue reached ***** billion U.S. dollars. Nvidia’s business overview Nvidia is a U.S. technology firm specializing in the design of graphics processing units (GPUs) for the gaming and professional markets, as well as system-on-chip units (SoCs). Headquartered in Santa Clara, California, the company was founded in 1993 by Jensen Huang who, following on from time spent as a microprocessor designer at Advanced Micro Devices (AMD), has been Nvidia’s president and CEO from the outset. Nvidia’s specialized markets In Nvidia’s 2025 fiscal year, the fourth quarter saw data center revenues climb to **** billion U.S. dollars, a surge that has seen it become the darling of stocks and a global leader in artificial intelligence (AI). Nvidia’s technologies and solutions are being deployed for accelerated computing and generative AI applications.

When comparing the data segment revenues of Nvidia, AMD, and Intel, it is clear that Nvidia has experienced extraordinary growth in recent quarters. In the fourth quarter of the 2024 calendar year, Nvidia generated **** billion U.S. dollars through its data center segment, a part of the business that includes graphics processing unit (GPU) sales. GPUs are used to train and run various large language models, most notably ChatGPT, the one developed by OpenAI.

Nvidia reported $24.74B in EBITDA for its fiscal quarter ending in January of 2025. Data for Nvidia | NVDA - Ebitda including historical, tables and charts were last updated by Trading Economics this last August in 2025.

In its 2025 fiscal year, Nvidia recorded revenues of ***** billion U.S. dollars, up from the **** billion U.S. dollars in 2024. The figure for fiscal year 2025 is also the highest for the company as it reaps the rewards from the artificial intelligence (AI) boom.

Nvidia reported $28.89B in Gross Profit on Sales for its fiscal quarter ending in January of 2025. Data for Nvidia | NVDA - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Analysts are optimistic as Nvidia prepares to release its Q4 earnings, with projections showing record revenue and AI chip demand driving confidence.

Nvidia reported $22.09B in Net Income for its fiscal quarter ending in January of 2025. Data for Nvidia | NVDA - Net Income including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Nvidia reported $25.22B in Pre-Tax Profit for its fiscal quarter ending in January of 2025. Data for Nvidia | NVDA - Pre Tax Profit including historical, tables and charts were last updated by Trading Economics this last August in 2025.

In its 2025 fiscal year, Nvidia's revenue from its Compute & Networking business segment amounted to about ***** billion U.S. dollars, whilst revenue from its Graphics segment amounted to **** billion U.S. dollars. Nvidia’s business segments The Compute & Networking segment includes data center platforms and systems for artificial intelligence (AI) and high-performance computing. The Compute & Networking segment also includes products that are being used in autonomous vehicles, robotics, and mobile devices. Meanwhile, Nvidia’s Graphics segment is aimed at specialized markets, including the GeForce series for gamers, as well as software products developed for cloud-based visual and virtual computing. Nvidia’s competitors Nvidia’s competitors in the GPU market include suppliers of both discrete and integrated graphics, with notable examples including AMD and Intel. Nvidia’s also faces competition from firms designing other accelerated computing solutions, particularly the growing number of startups specializing in AI chips, as well as larger tech firms like Alphabet, the parent company of Google, who are looking to innovate in the AI chips space through the development of their Tensor Processing Unit, or TPU.

Nvidia reported 47.4 in PE Price to Earnings for its fiscal quarter ending in April of 2025. Data for Nvidia | NVDA - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Super Micro's latest quarterly results reveal revenue increases but fall short of projections for adjusted earnings, impacting stock performance and raising market concerns.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NVIDIA: Still a Wise Investment? (NVDA)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Nvidia is set to report record fiscal Q1 results, with strong revenue growth driven by AI market dominance, despite facing export challenges in China.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nvidia's Earnings Show the Power of AI and Data Centers

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Nvidia reported $11.33B in Stock for its fiscal quarter ending in April of 2025. Data for Nvidia | NVDA - Stock including historical, tables and charts were last updated by Trading Economics this last August in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Nvidia Stock Price: A Game Theory Analysis

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data