Key Takeaways:
- Cloud AI chipset revenue is surging. The market will grow 65% in 2025 to reach $63.6 billion and is expected to hit $137 billion by 2031, nearly quadrupling in size from 2024.
- Generative AI is the main growth driver. The rise of Gen AI, cloud computing, and AI-optimized silicon is fueling record demand for powerful chipsets that can train and run large AI models.
- GPUs lead the market. Graphics Processing Units remain the top choice for cloud AI workloads, while ASICs are gaining traction and CPUs are growing more slowly.
- Energy use and scalability are key challenges. Power constraints and data center bottlenecks are pushing the industry toward greener designs, hybrid setups, and edge computing.
- Hybrid and multi-cloud strategies are essential. Businesses should invest in flexible cloud infrastructures and monitor fast-moving AI chipset innovations to stay competitive.
According to the latest forecast from ABI Research, the cloud AI chipset market size is set to grow 65% in 2025, reaching US$63.6 billion. Growing at a 21% Compound Annual Growth Rate (CAGR) between 2024 and 2031, the market is projected to reach US$137 billion by 2031. This translates to a nearly quadrupling of the market size compared to 2024,
The market is expanding rapidly, driven by advancements in Generative Artificial Intelligence (Gen AI), cloud computing, and AI-optimized silicon chipsets. Businesses and organizations are increasingly leveraging cloud AI to train complex Large Language Models (LLMs), run new applications, and integrate Machine Learning (ML) capabilities into their operations.
Table 1: Cloud AI Chipset Market Size by Architecture
| Revenue | Units | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 | CAGR 24-31 |
| CPU | (US$ Millions) | 6,579.2 | 7,459.1 | 8,373.7 | 9,419.2 | 10,514.1 | 11,563.9 | 12,718.0 | 13,602.3 | 11% |
| GPU | (US$ Millions) | 25,694.3 | 48,082.9 | 60,063.4 | 72,456.4 | 82,491.2 | 92,268.0 | 100,508.1 | 105,266.9 | 22% |
| FPGA | (US$ Millions) | 496.1 | 499.3 | 529.1 | 551.3 | 566.2 | 577.9 | 604.7 | 619.7 | 3% |
| ASIC | (US$ Millions) | 5,839.3 | 7,626.4 | 10,007.2 | 12,916.5 | 16,001.7 | 19,463.0 | 23,509.1 | 27,395.5 | 25% |
| Total | (US$ Millions) | 38,608.9 | 63,667.8 | 78,973.3 | 95,343.5 | 109,573.2 | 123,872.8 | 137,339.9 | 146,884.4 | 21% |
(Source: ABI Research)
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Defining the Cloud AI Market
The cloud AI market refers to cloud infrastructure that is deployed to support all AI workloads, namely training and inference, that take place in the cloud. ABI Research breaks down cloud AI into four main categories:
- Public Cloud: Refers to data center and related infrastructure maintained by cloud service providers, such as Amazon Web Services (AWS), Microsoft, Google, Alibaba, Baidu, and Tencent
- Enterprise Data Center: Refers to the part of a private cloud that is exclusively owned, maintained, and operated by private enterprises for their own needs. The data center can be located in a colocation center or a data center exclusively owned by the enterprise
- Hybrid Cloud: Refers to a combination of public and private cloud, supported by companies like Oracle, VMware, Rackspace, NetApp, and Cloudera.
- Telco Cloud: Refers to cloud storage, computing, and network infrastructure deployed by telecommunication service providers for their core network, Information Technology (IT), and edge computing workloads. At the moment, ABI Research expects that telco cloud deployments are still in the early stage and not capable of supporting telco AI applications. All telco AI workloads are still being performed in a public cloud.
What's Driving the Cloud AI Market?
The cloud AI market is experiencing enormous growth, particularly as Gen AI adoption continues to accelerate. Demand for AI chipsets is surging, with shipments expected to reach nearly 38 million units by 2031. Graphics Processing Units (GPUs) have become the dominant architecture for cloud AI workloads, favored for their ability to handle a broad range of AI workloads, from the most taxing training runs to smaller inference tasks.
While Application-Specific Integrated Circuits (ASICs) are also gaining significant traction, traditional Central Processing Units (CPUs) are expected to grow at a slower rate. They are forecast to make up a smaller share of the overall market. Field-Programmable Gate Arrays (FPGAs) will see the least growth for cloud AI solutions, as their adoption remains niche in comparison.
Cloud AI Market Challenges and Solutions
The market has faced challenges in scaling AI data centers, with concerns over energy consumption leading to delays in large-scale projects. As cloud AI workloads grow, power constraints and infrastructure bottlenecks are forcing companies to rethink deployment strategies. Indeed, there is immense urgency to build green data centers that can meet climate regulations, while accommodating computing-intensive AI applications.
To manage these challenges, cloud AI market players are increasingly shifting toward hybrid and edge computing solutions. Offloading inference workloads to edge devices and on-premises servers can help alleviate network congestion, provide relief to local electrical grids, and reduce latency.
At the same time, hyperscalers and cloud service providers continue to refine their AI offerings. Doing so will ensure that businesses can leverage advanced AI capabilities without excessive reliance on centralized data centers.
Get More Cloud AI Market Intelligence
As Gen AI adoption continues to surge, businesses will rely more on cloud AI for a wide range of applications, including those unsuitable to run at the edge. Indeed, not everyone will look to invest in private data centers and on-premises servers. However, scaling AI will require technical innovations in energy efficiency, edge computing, and hybrid cloud solutions.
To ensure long-term success with cloud AI deployments, ABI Research suggests that businesses take the following actions:
- Get organization-wide buy-in for AI-optimized cloud services.
- Invest in hybrid and multi-cloud strategies.
- Monitor the rapid advancements in AI chipsets and computing architectures.
Want more insights on the cloud AI market? Refer to ABI Research's Artificial Intelligence and Machine Learning: Cloud AI for a deeper analysis of the latest trends and market forecasts.
Frequently Asked Questions
What is the cloud AI chipset market size?
The cloud AI chipset market is valued at US$63.6 billion in 2025 and is expanding rapidly due to the rise of Generative AI and cloud computing. It is forecast to grow at a 21% Compound Annual Growth Rate (CAGR) through 2031, reaching US$137 billion.
What is the projection for the cloud AI market by 2030?
By 2030, the cloud AI market will nearly quadruple in size, driven by increasing demand for AI-optimized silicon and large-scale model training in the cloud. Growth will be fueled by advancements in GPUs, ASICs, and hybrid cloud solutions that support complex AI workloads.
Why are companies turning to cloud AI?
Businesses are adopting cloud AI to train Large Language Models (LLMs), run advanced AI applications, and integrate machine learning into daily operations. Cloud AI offers the flexibility, scale, and computing power needed to handle today’s data-intensive workloads.
What are the challenges facing the cloud AI market?
The cloud AI market faces major challenges around energy consumption, data center scaling, and infrastructure bottlenecks. Companies are responding by developing greener, hybrid, and edge computing solutions to reduce power demand, lower latency, and improve efficiency.