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The large-scale AI model market is expanding with rising adoption of generative AI, cloud infrastructure, and multimodal foundation models, while the U.S. segment is projected to grow from USD 5.40 billion in 2025 to USD 19.20 billion by 2035.
Austin, Feb. 11, 2026 (GLOBE NEWSWIRE) — The global large-scale AI model market was valued at USD 13.53 billion in 2025 and is expected to reach USD 52.82 billion by 2035, growing at a CAGR 14.6% over 2026-2035.
The market for large-scale AI models is expanding quickly due to the growing application of AI across industries, the growing demand for automation and data-driven decision-making, and the growing business acceptance of generative AI.
Large-Scale AI Models Market
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The U.S. large-scale AI model market is projected to grow from USD 5.40 Billion in 2025 to USD 19.20 Billion by 2035, at a CAGR of 10.36%.
Strong business acceptance of generative AI, significant hyperscaler expenditures, sophisticated cloud and GPU infrastructure, growing automation demand, and early AI integration across the BFSI, healthcare, and IT sectors are the main drivers of growth.
Segmentation Analysis:
By Model Type
In 2025, foundation models dominated with 39% share due to their broad applicability, scalability, and ability to support multiple downstream tasks. Multimodal models are the fastest-growing segment, driven by rising demand for AI systems that process text, images, audio, and video together.
By Deployment Mode
In 2025, cloud-based deployment dominated with 63% share due to scalability, flexibility, and cost efficiency. Hybrid deployment is the fastest-growing segment, driven by increasing demand for data security and regulatory compliance.
By Application
In 2025, natural language processing (NLP) dominated with 35% share due to widespread use in chatbots, virtual assistants, document processing, and enterprise automation, enabling improved customer engagement, workflow efficiency, and intelligent information extraction across industries. Generative AI is the fastest-growing application, driven by rapid adoption in content creation, code generation, marketing automation, and design.
By End-Use Industry
In 2025, IT & telecom dominated with 37% share due to early AI adoption, high data availability, and strong demand for network optimization, automation, and AI-driven customer support across large-scale digital infrastructures. Healthcare & life sciences are the fastest-growing end-use segment, supported by increasing use of large-scale AI models in diagnostics, medical imaging, drug discovery, and personalized treatment planning.
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Regional Insights:
The market for large-scale AI models in North America is expected to have the largest regional revenue share in 2025, with a projected 45% share. Early generative AI adoption, sophisticated cloud and GPU infrastructure, and a significant number of hyperscalers and AI developers are the main drivers of growth.
The fastest-growing region is Asia Pacific, which is anticipated to develop at a compound annual growth rate (CAGR) of 17.34% over the course of the projection period due to factors, such as increased cloud penetration, rapid digital transformation, and rising artificial intelligence investments.
Accelerating Enterprise Adoption of Generative AI to Augment Market Expansion Globally
The market for large-scale AI models is expanding as a result of growing automation needs, the quick enterprise adoption of generative AI, and the ongoing development of cloud and GPU infrastructure. Large-scale AI models are being incorporated into core operations by organizations in an effort to boost output, streamline processes, and increase decision-making precision. In 2025, more than 70% of major corporations are testing or implementing generative AI solutions in areas including operations, software development, analytics, and customer service, highlighting AI’s position as a key facilitator of corporate digital transformation.
Key Players:
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Microsoft Corporation
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Google LLC (Alphabet Inc.)
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OpenAI
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Amazon Web Services (AWS)
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NVIDIA Corporation
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Meta Platforms, Inc.
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IBM Corporation
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Oracle Corporation
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Baidu, Inc.
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Alibaba Group Holding Limited
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Tencent Holdings Limited
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SAP SE
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Salesforce, Inc.
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Anthropic
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Cohere
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Huawei Technologies Co., Ltd.
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Intel Corporation
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Samsung Electronics
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Palantir Technologies
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Stability AI
Recent Developments:
In May 2025: Microsoft announced expanded Azure AI Model Catalog capabilities, enabling enterprises to deploy and fine-tune large-scale AI models with enhanced governance and security features.
In April 2025: Google introduced enhanced multimodal Gemini model capabilities within Google Cloud, enabling improved reasoning, code generation, and enterprise AI customization.
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Exclusive Sections of the Report (The USPs):
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Enterprise Adoption & Deployment Metrics – helps you measure the percentage of enterprises using large-scale AI models for core business operations versus experimental use, along with sector-wise adoption growth across IT & telecom, BFSI, healthcare, retail, and government.
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Platform User Scale & Engagement Indicators – helps you evaluate market traction by analyzing the number of active enterprise users per large-scale AI platform on an annual basis.
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Model Architecture & Deployment Mix Analysis – helps you understand technology direction by assessing the share of foundation models, large language models, and multimodal models, along with cloud-based, hybrid, and on-premise deployment preferences.
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Advanced Model Enhancement Adoption – helps you identify innovation intensity by tracking integration rates of retrieval-augmented generation and fine-tuning across enterprise AI deployments.
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Performance, Cost & Governance Benchmarks – helps you compare inference latency, response accuracy, compute cost per training and inference cycle, and alignment with AI governance, data privacy, and regulatory frameworks.
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Enterprise Value Creation & ROI Insights – helps you assess real-world impact through analysis of enterprise task volumes processed, adoption of domain-specific fine-tuned models, satisfaction levels, ROI realization, and platform retention rates.
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