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.

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.

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 fiscal year 2025, NVIDIA Corporation's revenue by segment (products & services) are as follows: Automotive: $1.69 B, Data Center: $115.19 B, Gaming: $11.35 B, OEM And Other: $389.00 M, Professional Visualization: $1.88 B.

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.

In its 2025 fiscal year, Nvidia's revenue from data centers amounted to ***** billion U.S. dollars, whilst revenue from gaming amounted to **** billion U.S. dollars. Nvidia’s technologies and solutions are being deployed for accelerated computing and artificial intelligence applications AI, with Nvidia chips used to train and run various large language models, including ChatGPT.

Nvidia is poised for strong earnings with a forecasted 73% revenue surge, driven by high demand for Blackwell GPUs and sustained enterprise commitments from major tech giants.

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

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.

Data-Center GPU Market Outlook

According to our latest research, the global Data-Center GPU market size reached USD 17.6 billion in 2024, reflecting robust growth driven by escalating demand for accelerated computing in enterprise and hyperscale data centers. The market is exhibiting a strong compound annual growth rate (CAGR) of 32.5% from 2025 to 2033. By the end of 2033, the data-center GPU market is forecasted to achieve a remarkable value of USD 201.8 billion. This unprecedented expansion is primarily fueled by the surging adoption of artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) workloads across diverse industry verticals, as well as the exponential growth in data generation and analytics requirements.

One of the primary growth factors propelling the data-center GPU market is the rapid proliferation of AI and ML applications across sectors such as healthcare, finance, automotive, and manufacturing. GPUs have become the backbone for accelerating complex computations and deep learning processes, outperforming traditional CPUs in handling parallel processing tasks. Organizations are increasingly leveraging data-center GPUs to enable faster model training, real-time inference, and large-scale analytics, thereby gaining a competitive edge in innovation and operational efficiency. This shift is further amplified by the growing sophistication of AI models, which demand higher computational throughput and memory bandwidth—capabilities that modern GPUs are uniquely positioned to deliver.

Another significant driver for the data-center GPU market is the widespread adoption of cloud computing and the migration of enterprise workloads to cloud environments. Leading cloud service providers, such as AWS, Microsoft Azure, and Google Cloud, are integrating cutting-edge GPU solutions into their infrastructure to offer GPU-accelerated services for AI, graphics rendering, and HPC. This trend is democratizing access to high-performance GPUs, enabling organizations of all sizes to harness the power of accelerated computing without the need for substantial capital investment in on-premises hardware. The scalability, flexibility, and cost-effectiveness of cloud-based GPU deployments are further catalyzing market growth, particularly among small and medium enterprises (SMEs) and startups.

The data-center GPU market is also experiencing robust growth due to the increasing demand for real-time graphics rendering and immersive experiences in sectors such as media & entertainment, gaming, and virtual reality. As content creators and service providers strive to deliver ultra-high-definition (UHD) content, photorealistic visual effects, and interactive simulations, the need for powerful GPUs in data centers becomes paramount. Moreover, the rise of edge computing and the Internet of Things (IoT) is generating massive volumes of data that require rapid processing and analysis, further reinforcing the pivotal role of data-center GPUs in modern digital ecosystems.

Regionally, North America continues to dominate the data-center GPU market, accounting for the largest revenue share in 2024, followed closely by Asia Pacific and Europe. The presence of leading technology giants, early adoption of AI-driven solutions, and substantial investments in hyperscale data centers are key factors underpinning North America’s leadership. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, expanding cloud infrastructure, and increasing government initiatives to promote AI and HPC research. Europe is also witnessing steady growth, supported by advancements in industrial automation, smart manufacturing, and data-driven innovation. Latin America and the Middle East & Africa are gradually catching up, propelled by growing investments in IT modernization and digital infrastructure.

Product Type Analysis

The data-center GPU market is segmented by product type into Discrete GPU and Integrated GPU, each serving distinct computational needs within modern data centers. Discrete GPUs, which are standalone graphics processing units, have become the preferred choice for high-performance workloads such as AI training, deep learning, and scientific simulations. Their dedicated memory and processing power enable them to handle massive parallel computations efficiently, making them indispensable for hyperscale cloud provi

AMD's data center business struggles cast a shadow on its stock performance, highlighting challenges in AI computing and competition with Nvidia.

According to Cognitive Market Research, the global Data Center Chip Market size will be USD 208172.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 14.20% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 77023.94 million in 2025 and will grow at a compound annual growth rate (CAGR) of 12.0% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 60370.11 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 49961.47 million in 2025 and will grow at a compound annual growth rate (CAGR) of 16.2% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 7910.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 13.2% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 8326.91 million in 2025. It will grow at a compound annual growth rate (CAGR) of 13.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 4579.80 million in 2025. It will grow at a compound annual growth rate (CAGR) of 13.9% from 2025 to 2033.
The Artificial Intelligence (AI) chip category is the fastest growing segment of the Data Center Chip industry

Market Dynamics of Data Center Chip Market

Key Drivers for Data Center Chip Market

Increasing Demand for High-Performance Computing (HPC) and AI Applications in Data Centers

The increasing reliance on high-performance computing (HPC) and artificial intelligence (AI) applications is a major driver of growth in the Data Center Chip Market. As industries such as healthcare, finance, automotive, and retail adopt AI and machine learning technologies, the demand for chips that can handle vast amounts of data and complex computations continues to rise. Traditional CPUs often fall short of delivering the speed and efficiency required for these workloads, leading to a surge in the use of GPUs, TPUs, and other specialized processors. These chips are essential for supporting data-heavy operations like predictive analytics, real-time data processing, and AI model training. As businesses prioritize digital transformation and AI integration, the need for powerful and scalable chip solutions in data centres is expected to increase significantly.

Increased Adoption of AI and High-Performance Computing (HPC) in Data Centers

The demand for high-performance computing (HPC) and artificial intelligence (AI) is a significant driver of the Data Center Chip Market. With industries like healthcare, finance, and automotive relying heavily on AI and machine learning for data-driven insights, the need for specialized processors that can handle large-scale data and complex computations has grown substantially. Traditional processors such as CPUs are often inadequate to meet these demands, leading to the increased use of specialized chips like GPUs, TPUs, and FPGAs. These chips are critical for enabling fast data processing, real-time analytics, and AI model training. As businesses and industries undergo digital transformation, the need for scalable, powerful chip solutions to support AI workloads and HPC in data centres is expected to grow exponentially. The continuous development of AI technologies further amplifies the demand for chips tailored specifically to support AI and machine learning applications, making this a key growth driver in the data centre chip market.

Restraint Factor for the Data Center Chip Market

High Costs of Developing and Manufacturing Advanced Data Center Chips

Data Center Chip Market is the significant cost involved in developing and producing advanced chips. The design and manufacturing of specialized chips, such as GPUs, TPUs, and FPGAs, needed for high-performance computing (HPC) and artificial intelligence (AI) applications require substantial investments in research, technology, and specialized infrastructure. The complexity of these chips increases production costs due to the advanced materials and cutting-edge manufacturing techniques involved. These high costs can limit accessibility, especially for smaller businesses or those operating in emerging markets with budget constraints. As a result, while there is strong dem...

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.

Hewlett Packard Enterprise's Q2 revenue surpasses expectations due to high demand for AI-optimized servers and advanced data center solutions, boosting shares by 4%.

Explore the factors leading to a downturn in AI-linked tech stocks, with highlight on Nvidia's upcoming earnings and strategic industry shifts.

Worldwide spending on data center systems is projected to reach over, *** billion U.S. dollars in 2025, marking a significant ** percent increase from 2024. This growth reflects the ongoing digital transformation across industries and the increasing demand for advanced computing capabilities. The surge in data center investments is closely tied to the rapid expansion of artificial intelligence technologies, particularly with the wake of generative AI. AI chips fuel market growth The rise in data center spending aligns with the booming AI chip market, which is expected to reach ** billion U.S. dollars by 2025. Nvidia has emerged as a leader in this space, with its data center revenue skyrocketing due to the crucial role its GPUs play in training and running large language models like ChatGPT. The global GPU market, valued at ** billion U.S. dollars in 2024, is a key driver of this growth, powering advancements in machine learning and deep learning applications. Semiconductor industry adapts to AI demands The broader semiconductor industry is also evolving to meet the demands of AI technologies. With global semiconductor revenues surpassing *** billion U.S. dollars in 2023, the market is expected to approach *** billion U.S. dollars in 2024. AI chips are becoming increasingly prevalent in servers, data centers and storage infrastructures. This trend is reflected in the data centers and storage semiconductor market, which is projected to grow from ** billion U.S. dollars in 2023 to *** billion U.S. dollars by 2025, driven by the development of image sensors and edge AI processors.

According to Cognitive Market Research, the global AI Server Market size will be USD 143524.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 35.20% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 53104.18 million in 2025 and will grow at a compound annual growth rate (CAGR) of 33.0% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 41622.19 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 34445.95 million in 2025 and will grow at a compound annual growth rate (CAGR) of 37.2% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 5453.94 million in 2025 and will grow at a compound annual growth rate (CAGR) of 34.2% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 5740.99 million in 2025. It will grow at a compound annual growth rate (CAGR) of 34.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 3157.55 million in 2025. It will grow at a compound annual growth rate (CAGR) of 34.9% from 2025 to 2033.
Inference server category is the fastest growing segment of the AI Server industry

Market Dynamics of AI Server Market

Key Drivers for AI Server Market

Increasing Demand for High-Performance AI Computing Infrastructure Across Key Sectors

A major driver of the AI server market is the rising demand for advanced computing infrastructure as artificial intelligence becomes central to sectors like healthcare, finance, and autonomous technology. With the growing integration of generative AI and machine learning across industries, there is an urgent need for high-performance servers to support complex AI workloads. This trend is prompting major players such as Dell, HPE, and Lenovo to ramp up investments in AI server solutions. Lenovo, for instance, recently saw a significant boost in revenue—up by 20% in a single quarter—largely due to increased spending on AI infrastructure, helping the company outperform profit expectations and support a broader recovery in the tech sector.

Growing Demand for Cloud-Based AI Services and Its Impact on the AI Server Market

The expansion of cloud-based AI services is a key driver propelling the AI server market forward. As organizations increasingly adopt AI for business intelligence, automation, and customer engagement, cloud platforms offer a scalable and cost-effective solution to deploy these technologies without the need for extensive on-premise infrastructure. Major cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are investing heavily in AI-optimized server hardware to support the growing demand for machine learning and inference capabilities on the cloud. These platforms allow enterprises of all sizes to access powerful AI tools and infrastructure on a pay-as-you-go basis, democratizing the use of advanced AI. This trend is accelerating the deployment of AI servers in hyperscale data centres globally, fueling the growth of the overall market. Moreover, with the increasing integration of AI into cloud-native applications and services, the need for more efficient and high-performance AI servers is only expected to rise.

Restraint Factor for the AI Server Market

High Costs of Investment and Maintenance for AI Server Infrastructure

A key restraint in the AI server market is the high cost of both initial investment and ongoing maintenance of AI-optimized server infrastructure. Deploying AI solutions, including deep learning and large-scale data processing, demands specialized hardware, such as powerful GPUs and high-performance CPUs, which are often costly. Beyond the upfront capital expenditure, there are continuous operational costs related to energy consumption, cooling systems, and software licenses. While large organizations with substantial budgets can manage these expenses, smaller businesses or those with limited resources may find it difficult to justify the investment, hindering the broader adoption of AI server technologies across various sectors. Introduction of the AI Server Market

The AI server market refers to the specialized computing infr...

AMD's first-quarter earnings exceeded expectations with a 36% revenue increase, driven by a 57% surge in data center sales, highlighting strong growth potential.

Synchro Start GPU Market Outlook

According to our latest research, the global Synchro Start GPU market size reached USD 48.7 billion in 2024, driven by surging demand for high-performance graphics processing units across a range of industries. The market is expected to expand at a robust CAGR of 18.2% from 2025 to 2033, reaching a forecasted value of USD 229.9 billion by 2033. This significant growth is primarily attributed to escalating adoption of GPUs in artificial intelligence, gaming, and data center applications, as well as advancements in GPU architecture and processing capabilities.

One of the primary growth factors fueling the Synchro Start GPU market is the exponential rise in artificial intelligence and machine learning workloads. Modern AI models, particularly those used in deep learning, require immense computational power that only advanced GPUs can provide efficiently. Organizations across sectors such as healthcare, automotive, and finance are increasingly leveraging GPUs to accelerate data processing, enabling faster training and inference times for complex neural networks. This trend is further amplified by the proliferation of big data analytics and the shift towards automation, which collectively underscore the indispensable role of GPUs in next-generation computing environments.

Another significant driver is the flourishing gaming industry, which continues to push the boundaries of graphical fidelity and real-time rendering. Demand for discrete and hybrid GPUs has surged as game developers and consumers alike seek immersive experiences powered by technologies such as ray tracing, high refresh rates, and 4K resolution. The rise of eSports, virtual reality (VR), and augmented reality (AR) applications has also contributed to the market’s momentum, as these platforms require advanced GPUs for seamless performance and realistic visuals. In addition, the growing popularity of cloud gaming services is accelerating GPU adoption in data centers, further expanding the market’s reach.

In parallel, the expansion of data centers and cloud computing infrastructure is a crucial catalyst for Synchro Start GPU market growth. Enterprises are increasingly migrating workloads to the cloud, necessitating high-performance GPU solutions for tasks such as real-time analytics, scientific simulations, and professional visualization. The integration of GPUs in cloud-based platforms enhances scalability and flexibility, allowing organizations to meet fluctuating computational demands efficiently. Furthermore, advancements in GPU virtualization and multi-tenancy are enabling broader accessibility and cost-effectiveness, making GPUs a cornerstone technology for both public and private cloud environments.

From a regional perspective, North America continues to dominate the Synchro Start GPU market, accounting for the largest revenue share in 2024. This leadership is underpinned by the presence of major technology companies, strong R&D investments, and a vibrant ecosystem for AI and gaming innovation. However, the Asia Pacific region is rapidly emerging as a key growth engine, driven by increasing adoption of GPUs in consumer electronics, automotive, and enterprise applications. Europe and the Middle East & Africa are also witnessing steady growth, supported by digital transformation initiatives and expanding cloud infrastructure. Collectively, these regional dynamics highlight the global and multifaceted nature of the Synchro Start GPU market.

Product Type Analysis

The Synchro Start GPU market is segmented by product type into discrete GPUs, integrated GPUs, and hybrid GPUs, each catering to distinct performance and application requirements. Discrete GPUs are standalone graphics cards that offer the highest level of performance, making them the preferred choice for gaming enthusiasts, professional visualization, and data center workloads. Their dedicated memory and processing power enable superior rendering capabilities, which are essential for complex graphical tasks and compute-intensive applications. In 2024