The cloud AI 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.
According to the latest findings from ABI Research, the cloud AI chipset market is set to grow at a 23% Compound Annual Growth Rate (CAGR) through the rest of the decade, with revenue projected to reach nearly US$119 billion by 2030.
What Is Cloud AI?
Cloud AI refers to AI workloads that run on cloud infrastructure, including training, inference, and data processing. Organizations use cloud-based AI to develop ML models, analyze large datasets, and automate processes. ABI Research segments cloud AI into four main categories:
- Public Cloud: Hosted by hyperscale cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, public cloud platforms offer AI services at scale.
- Enterprise Data Centers: Privately owned infrastructure that allows organizations to maintain control over their AI workloads.
- Hybrid Cloud: A mix of public and private cloud environments, supported by companies like Oracle and VMware, enabling flexibility in AI deployment.
- Telco Cloud: Infrastructure deployed by telecommunications providers for core networks and edge computing. However, telco AI workloads remain in their early stages and are primarily handled in the public cloud today. Telefónica’s Tech Cloud Platform is a prominent example, offering a sovereign cloud solution that provides much-needed flexibility for organizations as they scale their computing capabilities.
Related ABI Insight: Cloud AI Market Update: NVIDIA’s Cloud Strategy, Hyperscalers' ASICs, and DeepSeek
The Role of Cloud Computing in AI
Cloud computing is the backbone of AI development and deployment, providing the computational power necessary for model training and inference. Before the rise of cloud AI, companies relied on expensive on-premises hardware to run AI applications. Now, cloud computing offers scalability, cost efficiency, and faster deployment.
With cloud computing, businesses can scale or reduce their AI workloads on demand, eliminating the need for upfront hardware investments, while reducing costs. Developers also gain access to pre-trained models, cloud-based Graphics Processing Units (GPUs), and optimized AI toolkits, streamlining the process of deploying AI solutions.
Cloud AI Market Dynamics
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 30 million units by 2030. 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, making up a smaller share of the overall market. Field-Programmable Gate Arrays (FPGAs) will see the least growth, as their adoption remains niche in comparison.
Geographically, Asia-Pacific continues to lead in demand for cloud AI infrastructure, with strong adoption in technologically advanced countries like China, Japan, Taiwan, and Singapore. Recent U.S. trade restrictions on high-performance AI chips have impacted shipments to China. This trend is particularly impactful on major AI chip providers such as NVIDIA, AMD, and Intel. However, as is the case with DeepSeek, these restrictions have not stopped some Chinese tech players from getting their hands on Western silicon.
The industry has also 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 is 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 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.
NVIDIA’s continued dominance in the AI market further underscores the industry’s trajectory. Throughout 2024, the company saw sustained revenue growth, supported by strong demand for its AI chipsets. This momentum reflects the broader shift toward AI-driven cloud computing, where enterprises and cloud service providers race to scale AI workloads.
Cloud AI Is Here to Stay
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.
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