Opinions seem to differ on the importance of language models and therefore graphics processor unit operations provided “as a service” by cloud computing as a service giants including Amazon Web Services, Google Cloud and Azure, for example.
Current forecasts suggest that the single-digit billions (U.S. dollars) GPU as a service market could grow to about $36 billion in annual revenues within five years. While significant, such revenue forecasts might not be so large in relation to the capital investment being made to support GPU as a service capabilities.
GPU as a Service Market Estimates (2025-2030) |
Year | Global Market Size (USD Billions) | U.S. Market Size (USD Billions) | Global Growth Rate (%) | U.S. Growth Rate (%) | Sources |
2025 | $8.20 | $3.40 | 31.50% | 29.80% | Grand View Research, MarketsandMarkets |
2026 | $11.10 | $4.50 | 35.40% | 32.40% | Gartner, IDC |
2027 | $15.20 | $6.10 | 36.90% | 35.60% | Forrester Research, Allied Market Research |
2028 | $20.70 | $8.30 | 36.20% | 36.10% | Mordor Intelligence, Technavio |
2029 | $27.50 | $10.90 | 32.90% | 31.30% | Emergen Research, IMARC Group |
2030 | $35.80 | $14.10 | 30.20% | 29.40% | Fortune Business Insights, Research and Markets |
But GPU as a service is a subset of “AI as a service,” which is generally an order of magnitude greater than GPU as a service in any given year.
AI as a Service Market Revenue Estimates (2025-2030) |
Year | Global Revenue (USD Billions) | U.S. Revenue (USD Billions) | Global Growth Rate (%) | U.S. Growth Rate (%) | Sources |
2025 | $26.50 | $11.80 | 34.20% | 32.60% | Based on pre-2025 trends from Markets and Markets, Grand View Research |
2026 | $36.80 | $16.10 | 38.90% | 36.40% | Projections extrapolated from Gartne³, IDC forecast models |
2027 | $52.10 | $22.40 | 41.60% | 39.10% | Aligned with long-term trends identified by Allied Market Research |
2028 | $73.50 | $31.20 | 41.10% | 39.30% | Derived from Mordor Intelligence⁶, Technavio market analyses |
2029 | $101.90 | $42.80 | 38.60% | 37.20% | Based on growth curves projected by Emergen Research |
2030 | $138.60 | $57.90 | 36.00% | 35.30% | Consistent with Fortune Business Insights, Research and Markets |
It also is fair to note that observers disagree a bit about which industry verticals will be the biggest users and beneficiaries of GPU as a service, as is the case also for estimates of which industries are likely to be the greatest beneficiaries and users of AI as a service.
There seems general agreement that some industries will probably not be big users of either AI or GPU as a service. Agriculture, construction, hospitality and tourism, mining and government tend to be among those industries.
Industry Vertical | Primary Operations | Why Unlikely to Be High GPUaaS Users | Key Constraints |
Agriculture, Forestry | Crop production, livestock management, forestry operations | Limited use of LLMs or AI; focus on IoT and basic analytics rather than compute-intensive tasks | Low data complexity, cost sensitivity, and limited scalability of AI applications |
Construction | Building infrastructure, urban development, heavy machinery operations | Minimal reliance on LLMs; AI use limited to project management and basic design, not GPU-intensive | High capital costs prioritize physical assets over advanced compute infrastructure |
Hospitality, Tourism | Hotels, restaurants, travel services, event management | Basic AI for customer service (e.g., chatbots) doesn’t require extensive GPU resources; low data volume | Focus on human-centric services, limited need for real-time complex processing |
Public Administration | Government services, regulatory compliance, public policy implementation | Constrained by budgets and data privacy; AI use limited to basic automation, not large-scale LLM training | Bureaucratic inertia, regulatory restrictions, and preference for on-premises systems |
Education (Traditional) | K-12 schools, universities, vocational training | Limited use of LLMs beyond administrative automation; cost barriers and focus on human-led instruction | Budget constraints, low computational needs, and ethical concerns around AI use |
Mining, Quarrying | Mineral extraction, resource exploration, heavy equipment operations | AI limited to predictive maintenance and geospatial analysis, not requiring high GPU compute | Harsh environments, focus on physical operations, and low data-driven workflows |
Waste Management, Remediation | Waste collection, recycling, environmental cleanup | Minimal AI adoption; basic analytics for logistics don’t demand GPUaaS | Low-margin industry, limited scalability of AI, and focus on operational efficiency |
But some industry verticals are generally considered to be heavier users. Drug discovery in the pharmaceutical industry; fraud detection and risk modeling in financial services and code generation in the technology business provide examples.
Autonomous vehicles and content creation are other verticals where heavy use of AI is likely or necessary.
Industry Vertical | Primary GPUaaS Use Cases | Key Drivers for Adoption | Estimated Impact |
Healthcare, Life Sciences | Drug discovery, genomic analysis, medical imaging, virtual health assistants | Faster drug development, improved diagnostics, personalized medicine | High: Accelerates life-saving innovations and reduces R&D costs |
Financial Services | Fraud detection, risk modeling, algorithmic trading, customer service automation | Low-latency predictions, regulatory compliance, competitive edge | High: Enhances accuracy and speed in high-stakes transactions |
Technology, Cloud Services | NLP, code generation, enterprise AI platforms, cloud-based AI services | Scalable AI infrastructure, market leadership in AI services | Very High: Powers AI ecosystems and serves other industries |
Media, Entertainment | Content creation, video transcoding, digital avatars, gaming AI | Demand for immersive, personalized content and real-time processing | Moderate-High: Drives innovation in creative and gaming sectors |
Automotive, Transportation | Autonomous driving, traffic optimization, vehicle design simulation | Safety, scalability of autonomous systems, synthetic data generation | High: Critical for advancing self-driving technology |
Retail, E-commerce | Chatbots, personalized marketing, inventory management, sentiment analysis | Enhanced customer experience, operational efficiency | Moderate-High: Boosts sales and customer satisfaction |
Telecom | Network optimization, virtual assistants, predictive maintenance | Cost reduction, improved service quality, data-heavy operations | Moderate: Enhances efficiency in a competitive, data-driven industry |
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