The GPU Database Market Report is Segmented by Component (Solution, and Services), Deployment (Cloud, and On-Premises), End-User (BFSI, IT and Telecom, Retail and E-Commerce, and More), Application (Real-Time Analytics and BI, Fraud Detection and Risk Analytics, and More), Data Model (Column-Store, Document / Vector, Graph, and Multimodal), and Geography.

Techpowerup datasets

Dataset consists of two datasets, cpus and gpus scraped using Python scrapy from https://www.techpowerup.com/.

CPU Dataset

The CPU dataset contains information about various CPU models and their specifications. The dataset includes the following columns:

• Name: The name of the CPU model.
• Codename: The codename used by the manufacturer for the CPU model.
• Cores: The number of cores in the CPU.
• Clock: The base clock speed of the CPU, measured in GHz.
• Socket: The socket type that the CPU is compatible with.
• Process: The manufacturing process used to create the CPU, measured in nanometers.
• L3 Cache: The size of the L3 cache in the CPU, measured in MB.
• TDP: The thermal design power of the CPU, measured in watts.
• Released: The release date of the CPU.

GPU Dataset

The GPU dataset contains information about various GPU models and their specifications. The dataset includes the following columns:

• Product_Name: The name of the GPU model.
• GPU_Chip: The GPU Chip that is used in the GPU Model
• Released: The release date of the GPU.
• Bus: The bus width of the GPU.
• Memory: The memory capacity of the GPU, measured in GB.
• GPU_clock: The base clock speed of the GPU, measured in MHz.
• Memory_clock: The memory clock speed of the GPU, measured in MHz.
• Shaders_TMUs_ROPs: The number of shaders, texture mapping units, and raster operations pipelines in the GPU.

Both of the datasets are useful for comparing the performance and features of different CPU and GPU models. They can be used for a variety of applications such as gaming, content creation, AI, Machine learning, and more. It could be used by researchers to study the evolution of the technology in a specific period of time and make predictions for future advancements. It could also be used by professionals in the tech industry, to make informed decisions when choosing components for a build or a system.

The code for the scraper can be found here

Graphic Processing Unit (GPU) Market size was valued at USD 77.39 Billion in 2024 and is projected to reach USD 638.61 Billion by 2032, growing at a CAGR of 33.30% from 2026 to 2032.Key Market Drivers: Gaming: The demand for high-performance GPUs for gaming is a major driver, fueled by the increasing popularity of high-resolution gaming, esports, and virtual reality (VR) and augmented reality (AR) experiences.Artificial Intelligence (AI) and Machine Learning (ML): GPUs are essential for training and deploying AI/ML models, driving demand from industries like data centers, research institutions, and autonomous vehicles.Data Centers: The growth of cloud computing and the increasing reliance on data centers for various applications, including AI, big data analytics, and high-performance computing (HPC), is significantly driving GPU demand.Scientific Computing: GPUs are used in various scientific fields, such as research in physics, chemistry, and biology, for simulations, data analysis, and image processing.Cryptocurrency Mining: While fluctuating in importance, cryptocurrency mining has historically been a significant driver of GPU demand.

In 2023, the high-density server graphics processing unit (GPU) market was valued at 9.8 billion U.S. dollars. Forecasts suggest that by 2028, this is likely to rise to 38.5 billion U.S. dollars. Comparatively, the dual to quad server GPU market is expected to reach 20.41 billion U.S. dollars in 2028.

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Graphics Processing Unit (GPU) Market Size 2025-2029

The graphics processing unit (gpu) market size is forecast to increase by USD 738 billion, at a CAGR of 59.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for advanced gaming and Virtual Reality (VR) experiences. These applications require high-performance graphics capabilities, leading to a surge in demand for more powerful GPUs. Additionally, the rise in demand for High-Performance Computing (HPC) applications, such as scientific simulations and machine learning, is also fueling market growth. However, challenges persist in the form of difficulties in upgrading GPUs in notebooks due to size and power constraints. Companies seeking to capitalize on market opportunities should focus on developing compact, power-efficient GPUs that can meet the demands of both gaming and HPC applications. Navigating the challenges of GPU upgrading in notebooks will require innovative solutions and collaboration between hardware manufacturers and OEMs. Overall, the GPU market presents significant opportunities for growth, particularly in the areas of gaming, VR, and HPC, while also posing challenges that require strategic solutions.

What will be the Size of the Graphics Processing Unit (GPU) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe GPU market continues to evolve, driven by advancements in technology and diverse applications across various sectors. High-end PCs integrate GPUs for texture filtering and game development, while AI chips utilize these components for deep learning acceleration. Cooling solutions and power management are crucial considerations for managing the increased power consumption of modern GPUs. Gaming consoles and server farms leverage GPU architecture for compute performance, with streaming multiprocessors and shader units enhancing graphics rendering and parallel processing capabilities. Memory bandwidth and clock speeds are essential factors in scientific computing and professional workstations. Data centers employ GPU clusters for data analytics, machine learning, and floating-point operations, while virtual reality and cloud gaming require GPUs for rendering complex graphics and handling real-time processing. HBM2E and tensor cores are among the latest innovations, offering improved memory efficiency and neural network acceleration. The graphics pipeline undergoes constant refinement, with ray tracing and compute shaders pushing the boundaries of visual realism. Thermal throttling and fan noise are ongoing concerns, necessitating advancements in cooling technology. The GPU market's continuous dynamism underscores its significance in driving technological progress and innovation.

How is this Graphics Processing Unit (GPU) Industry segmented?

The graphics processing unit (gpu) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeIntegrated GPUsDiscrete GPUsApplicationComputersTablets and smartphonesTelevisionGaming consolesEnd-userElectronicsIT and telecomDefense and intelligenceMedia and entertainmentOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACAustraliaChinaIndiaJapanSouth KoreaRest of World (ROW).

By Type Insights

The integrated gpus segment is estimated to witness significant growth during the forecast period.The market continues to evolve with advancements in technology, as integrated GPUs become more prevalent. Unlike discrete GPUs, integrated GPUs are part of the processor and utilize system memory shared with the Central Processing Unit (CPU). This results in reduced power consumption and heat generation, extending battery life. In January 2024, AMD introduced the Ryzen 8000G series, which includes the Ryzen 7 8700G CPU and the Radeon 780M integrated GPU. This solution offers enhanced performance for gaming and graphics-intensive tasks, surpassing entry-level discrete GPUs while maintaining energy efficiency. Advancements in GPU architecture include texture filtering, which improves image quality, and ray tracing, which creates more realistic lighting effects in games and virtual reality (VR). Deep learning acceleration, powered by tensor cores and streaming multiprocessors, enables faster machine learning and artificial intelligence (AI) processing. High-bandwidth memory (HBM2E) and memory clock speeds enhance memory access and data analytics capabilities. Compute performance is a critical factor, with CUDA cores and shader units enabling parallel processing for scientific computing, professional workstations, and high-performance computing (HPC) applications. Data centers and server farms re

Comprehensive database of graphics card specifications including memory, clock speeds, power requirements, and performance metrics for NVIDIA, AMD, and Intel GPUs

GPU Market Outlook

The global GPU market is projected to reach a valuation of USD 230 billion by 2032, growing at a CAGR of 23.5% from 2024 to 2032. This impressive growth is driven by the increasing adoption of GPUs across various industries, including gaming, data centers, and automotive sectors, among others.

One of the primary growth factors of the GPU market is the exponential rise in the demand for high-performance computing (HPC) and artificial intelligence (AI). GPUs are uniquely designed to handle multiple tasks simultaneously, making them ideal for applications requiring massive parallel processing capabilities. With AI and machine learning becoming integral components of numerous industries, the need for efficient and powerful GPUs is accelerating. Companies are investing heavily in research and development to innovate and enhance GPU capabilities, further driving their adoption across various sectors.

Another significant factor contributing to the growth of the GPU market is the burgeoning gaming industry. The continuous advancements in gaming technologies, including virtual reality (VR) and augmented reality (AR), demand high-end graphics processing units to deliver immersive experiences. The increasing popularity of eSports and online multiplayer games has also necessitated the development of more powerful GPUs, thereby fueling market growth. As gaming enthusiasts seek ever-better performance and realism in their gaming experiences, the demand for advanced GPUs is expected to surge.

The evolution of autonomous vehicles and advanced driver-assistance systems (ADAS) presents another pivotal growth driver for the GPU market. Automotive manufacturers are integrating sophisticated GPUs into their vehicles to handle the vast amounts of data generated by sensors and cameras, enabling real-time processing and decision-making. This advancement not only enhances vehicle safety but also supports the development of fully autonomous driving technologies. As the automotive industry continues to innovate, the demand for powerful GPUs is poised to escalate further.

Regionally, the Asia Pacific region is expected to dominate the GPU market, driven by the presence of leading GPU manufacturers and the rapid adoption of advanced technologies in countries like China, Japan, and South Korea. This region's strong focus on technological innovation, coupled with substantial investments in AI and machine learning, positions it as a key player in the global GPU market. North America and Europe are also anticipated to exhibit significant growth, with extensive applications of GPUs in data centers and automotive industries. The Middle East & Africa and Latin America regions, while still emerging, are likely to witness steady growth due to increasing investments in IT infrastructure and smart technologies.

Component Analysis

The GPU market can be segmented based on components, which include hardware, software, and services. The hardware segment is anticipated to hold the largest share, primarily due to the increasing demand for high-performance GPUs in various applications such as gaming, data centers, and automotive sectors. Hardware components, including graphic cards and GPU chips, are fundamental to the performance and efficiency of GPU systems. The continuous advancements in GPU hardware, such as the development of more powerful and energy-efficient chips, are expected to drive this segment's growth. Additionally, the rising trend of custom-built gaming PCs and workstations is further propelling the demand for GPU hardware.

Software components also play a crucial role in the functioning and optimization of GPUs. GPU software includes drivers, APIs, and other programs that enable the efficient operation and utilization of GPU hardware. With the increasing complexity of applications requiring GPUs, the demand for sophisticated software solutions that can maximize the potential of GPU hardware is rising. Companies are focusing on developing software that enhances the performance of GPUs in various applications, including gaming, AI, and machine learning. This segment is expected to grow significantly as the need for advanced GPU software solutions continues to expand.

The services segment encompasses a range of offerings, including installation, maintenance, and support services for GPU systems. As organizations increasingly adopt GPU technology, the demand for professional services to ensure the optimal performance and longevity of GPU systems is growing. Service providers offer spec

This dataset contains the latest prices and historical lowest prices for various GPU models from Nvidia, AMD, and Intel as of 2024. Sourced from Tom's Hardware, this data is useful for analyzing price trends and studying the cost dynamics of computer hardware.

Graphics Processing Unit (GPU) Market is Segmented by GPU Type (Discrete GPU, Integrated GPU, and Others), Device Application (Mobile Devices and Tablets, Pcs and Workstations, and More), Deployment Model (On-Premises and Cloud), Instruction-Set Architecture (x86-64, Arm, and More), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

GPU-Disaggregated DLRM Trace Dataset

This dataset records latency-sensitive inference instances for GPU-disaggregated serving of deep learning recommendation models. It contains per-instance resource reservations and life cycle timestamps for scheduling analysis and capacity planning.

This dataset represents a groundbreaking trace collection from production GPU-disaggregated serving systems for Deep Learning Recommendation Models (DLRMs), accompanying the NSDI'25 paper on GPU-disaggregated serving at scale. The dataset captures real-world operational characteristics of inference services in a large-scale production environment, providing invaluable insights into resource allocation patterns, temporal dynamics, and system behavior for latency-sensitive ML workloads.

Scope

• Total rows: 23871.
• Unique apps: 156.
• Role split, {'CN': 16485, 'HN': 7386}.

Key fields

Instance ID. Role. Application group. Requests and limits for CPU, GPU, RDMA, memory, and disk. Density cap per node. Creation, scheduling, and deletion timestamps relative to the trace start.

High-level observations

• All workloads are marked latency sensitive. Instances are typically long-running with high priority, as stated by the authors.
• Scheduling delay distribution and runtime distribution are included in the figures. Concurrency over time gives a view of system load.
• RDMA percentage and max instances per node expose placement constraints that influence packing on heterogeneous nodes.

🎯 Key Characteristics

Scale and Scope

• Total Instances: 23,871 inference instances
• Services: 156 unique inference services (applications)
• Workload Type: 100% Latency-Sensitive (LS) workloads
• Priority Level: High-priority, long-running inference instances
• System Architecture: GPU-disaggregated architecture separating compute and GPU resources

Instance Distribution

• CPU Nodes (CN): 16,485 instances (69.1%)
  • Pure CPU-based inference workloads
  • No GPU allocation
  • Lower RDMA requirements (mean: 3.4%)
• Heterogeneous GPU Nodes (HN): 7,386 instances (30.9%)
  • GPU-accelerated inference workloads
  • All instances allocated exactly 1 GPU
  • Higher RDMA requirements (mean: 20.5%)

🔍 Key Insights

Workload Heterogeneity

• Clear bimodal distribution between CPU and GPU workloads
• CN instances optimized for CPU-intensive operations
• HN instances balanced for GPU acceleration with supporting CPU resources

Resource Efficiency

• Tight coupling between CPU and memory allocation (correlation: 0.97)
• Independent scaling of GPU resources from CPU/memory
• RDMA bandwidth scaled based on disaggregation communication needs

Production Patterns

• All workloads classified as latency-sensitive
• High-priority, long-running inference services
• Immediate scheduling indicates sufficient resource availability

Disaggregation Benefits

• Efficient resource utilization through separation of concerns
• CN nodes handle CPU-intensive preprocessing/postprocessing
• HN nodes focus on GPU-accelerated model inference
• RDMA enables efficient data movement between disaggregated components

📈 Research Applications

This dataset enables research in:

• Resource Allocation: Optimal scheduling strategies for disaggregated systems
• Performance Modeling: Understanding latency-throughput tradeoffs
• System Design: Architectural decisions for ML serving infrastructure
• Workload Characterization: Production DLRM inference patterns
• Capacity Planning: Resource provisioning for ML workloads
• Fault Tolerance: Instance distribution and anti-affinity strategies

🎓 Academic Contribution

This dataset represents one of the first publicly available production traces for GPU-disaggregated DLRM serving, providing:

• Real-world validation data for system research
• Baseline for performance comparisons
• Foundation for reproducible research in ML systems
• Insights into production-scale ML infrastructure

This dataset provides a unique window into production GPU-disaggregated systems, offering researchers and practitioners valuable insights for advancing the field of large-scale ML serving infrastructure.

76 Global import shipment records of Gpu with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The GPU Database market is experiencing significant growth, with a CAGR of 16.8% during the forecast period of 2025-2033. The market is estimated to reach a value of million USD by 2033, driven by the increasing adoption of AI and machine learning applications that require high-performance computing. Key trends in the GPU Database market include the rise of cloud-based GPU databases, the adoption of GPU databases by various industry verticals such as financial services, healthcare, and retail, and the development of innovative technologies that improve the performance and efficiency of GPU databases. Restraints to market growth include the high cost of GPU hardware and the lack of skilled professionals to manage and operate GPU databases.

As of August 2025, NVIDIA's RTX PRO 6000 Blackwell Workstation Edition achieved the best average Vulkan performance among graphics processing units (GPUs) with a score of 410,425 from the Geekbench benchmarking tests. Of the ten highest scoring GPU Vulkan performances, Nvidia accounted for all GPUs.

GPU Database Market was valued at USD 4.23 billion in 2023 and is expected to reach USD 8.63 billion by 2029 with a CAGR of 12.45% during the forecast period.

Data-Center GPU Market Outlook

According to our latest research, the global Data-Center GPU market size reached USD 15.7 billion in 2024. The market is experiencing robust expansion, with a recorded CAGR of 24.2% from 2025 to 2033. This strong growth trajectory is primarily fueled by surging demand for accelerated computing in artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) applications. Based on our projections, the Data-Center GPU market size is expected to achieve USD 116.2 billion by 2033, reflecting the transformative impact of advanced GPU architectures and increasing cloud adoption on the data-center ecosystem.

A primary growth factor for the Data-Center GPU market is the exponential rise in AI and ML workloads across enterprises and research institutions. As organizations strive to extract actionable insights from massive datasets, the need for parallel processing and accelerated computation has never been more pronounced. Data-center GPUs, with their ability to handle thousands of concurrent threads, are uniquely positioned to address the computational intensity of deep learning, natural language processing, and predictive analytics. The proliferation of generative AI models, recommendation systems, and autonomous technologies is further amplifying the adoption of GPUs, as traditional CPUs are increasingly unable to meet the performance demands of modern data-driven applications. This trend is expected to intensify as AI becomes an integral component of business strategy across verticals.

Another significant driver is the ongoing shift toward cloud-based infrastructure, which has fundamentally altered the way organizations deploy and scale their computing resources. Cloud service providers are investing heavily in GPU-powered data centers to offer on-demand, scalable, and cost-effective access to high-performance computing resources. This democratization of GPU acceleration allows even small and medium-sized enterprises to leverage advanced AI and analytics capabilities without the need for substantial capital investment in hardware. The availability of GPU-accelerated cloud services from major providers such as AWS, Microsoft Azure, and Google Cloud Platform is accelerating innovation and enabling rapid experimentation, further boosting the demand for data-center GPUs worldwide.

Additionally, the rapid growth of data-intensive applications beyond AI, such as real-time graphics rendering, scientific simulations, and complex data analytics, is driving the need for high-throughput processing in data centers. Industries ranging from media & entertainment to healthcare and financial services are leveraging GPU acceleration to enhance visualization, improve decision-making, and deliver personalized experiences. The increasing adoption of virtual desktop infrastructure (VDI), remote work solutions, and immersive technologies such as virtual and augmented reality is also contributing to the sustained demand for powerful GPUs in data-center environments. As organizations prioritize digital transformation and operational agility, the strategic importance of data-center GPUs continues to rise.

From a regional perspective, North America remains the dominant force in the Data-Center GPU market, owing to its mature technology ecosystem, early adoption of AI and cloud computing, and significant investments in data-center infrastructure. However, the Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding cloud services, and government initiatives to foster innovation in AI and HPC. Europe is also witnessing strong growth, particularly in sectors such as automotive, healthcare, and fintech, where advanced analytics and simulation capabilities are critical. Latin America and the Middle East & Africa are gradually increasing their market share, driven by growing enterprise IT spending and the expansion of data-center networks to support regional digital economies.

Product Type Analysis

The &l

Unlock data-backed intelligence on India GPU Market, size at USD 115 in 2023, showcasing industry analysis and key players.

The Report Covers Global Gaming GPU Market Growth & Analysis. The Market is Segmented by Type (Dedicated Graphic Cards, Integrated Graphics Solutions), Device (Mobile Devices, PCs & Workstations, Gaming Consoles, Automotive), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa). The market size and forecasts are provided in terms of value (in USD million) for all the above segments.

Global GPU Database Market size is set to expand from $ 509.36 Million in 2023 to $ 2368.35 Million by 2032, with an anticipated CAGR of around 18.62% from 2024 to 2032.

Real-time collection of graphics card prices, specifications, and performance metrics across different retailers