Analyst(s): Ron Westfall
Publication Date: December 17, 2024
Cloud service providers are the dominant players in investing across AI processors and accelerators for both training and inference purposes and driving AI use cases.
Key Points:
- Cloud service providers are investing substantially in AI processors and accelerator technology to meet the fast-growing demand for AI computing power. AI workloads, particularly those involving the training of LLMs and other deep learning tasks, demand substantial computational resources that traditional CPUs and data center architectures struggle to scale and perform more efficiently.
- Hyperscalers are focusing their investments on enhancing AI processor and accelerator capabilities, including GPUs, CPUs, XPUs, and public cloud AI instances, which outperform traditional CPUs in processing AI workloads, thanks to their parallel processing power and specialized architecture.
- The top five AI use cases identified by Futurum Intelligence consist of visual & audio analytics, simulation and modeling, text analysis, generation & summation, predictive analytics, and GenAI. They are expected to be major growth drivers due to their real-time processing demands and vast data analysis needs.
Overview:
Cloud service providers (CSPs) are investing heavily in AI processors and accelerator technology to meet the fast-growing demand for AI computing power. AI workloads, especially for training large language models (LLMs) and other deep learning tasks, require massive computational resources that traditional CPUs and data center architectures cannot efficiently handle. To offer superior performance for AI tasks, CSPs (i.e., hyperscalers) are prioritizing their investment in AI accelerator capabilities across GPUs, CPUs, XPUs, and public cloud AI instances that can process AI workloads much faster than traditional CPUs due to their parallel processing capabilities and specialized architecture.
The Futurum Group’s latest insights report, The Cloud’s Leading Role in AI Processor Investments and Use Cases spotlights how Hyperscalers play an integral role in using AI processors and accelerators to scale AI use cases such as LLM training and inferencing. AI LLM training is the process of developing sophisticated AI systems capable of understanding and generating human language. Following the training phase, when the model learns from labeled datasets, the next second stage in the AI process is AI inference. Inferencing is the process of applying a trained machine learning (ML) model to new, unseen data to make predictions or decisions. Hyperscalers’ parallel processing capabilities are essential for both phases of deploying and running complex language models.
Futurum Intelligence data indicates the global AI processor and accelerator market is set to experience rapid expansion, driven by increasing demand for AI and machine learning applications, cloud-based services, and evolving competitive dynamics. This is a major impetus for why cloud providers need to use AI to ensure that rapidly expanding bandwidth demands such as AI workloads do not undercut AI use case support and agility.
According to Futurum Intelligence data, hyperscalers followed by enterprise and data center providers are the end users of this overall market. Hyperscalers are the dominant players as they prioritize high-performance, custom solutions, such as Google’s Tensor Processing Units (TPUs) for massive data processing in cloud services. Hyperscalers have solidified their dominance in the cloud computing market, with their combined Q1 2024 revenue reaching $9.0 billion.
The top AI use cases identified consist of visual & audio analytics, simulation and modeling, text analysis, generation & summation, predictive analytics, and GenAI. They are expected to be major growth drivers due to their real-time processing demands and vast data analysis needs. Niche applications such as quality assurance and simulation might see slower adoption due to cost and complexity considerations.
Key takeaways from The Cloud’s Leading Role in AI Processor Investments and Use Cases include:
- AI Processor and Accelerator Development Essential to CSP Strategy: Cloud providers are expanding their investment in custom AI chips and accelerators to optimize performance for AI workloads, providing assurance that their cloud platforms can scale AI training and inferencing workload demands with efficiency and reliability for all customers.
- Expansion of AIaaS: Expect more CSPs to offer AI capabilities on a pay-as-you-go basis, including AutoML platforms, AI-powered analytics, and natural language processing services that take advantage of underlying AI processor and accelerator technologies.
- Focus on AI Edge Capabilities: CSPs will prioritize enhancing their edge computing capabilities to ensure faster processing and real-time analysis for supporting rapidly evolving AI use cases across hybrid and on-premises environments, including popular GenAI, visual and audio analytics, and simulation and modeling applications.
CSPs are increasingly leveraging AI processors and accelerators to optimize AI workloads and augment their service offerings. Major players such as AWS, Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure (OCI) have invested heavily in diversifying their AI silicon portfolios. This includes developing custom-built AI accelerator ASICs, allowing them to tailor hardware solutions to their specific AI workload requirements, optimize performance, and reduce costs.
AI processors and accelerators are playing a crucial role in driving AI use cases across various industries by enabling faster, more efficient, and cost-effective processing of AI workloads. These specialized chips are transforming the way AI applications are developed and deployed, across the continuum of AI workload environments, including edge computing and large-scale data centers.
If you are interested in learning more, be sure to download your copy of The Cloud’s Leading Role in AI Processor Investments and Use Cases today. The full report is available via subscription to the Futurum Intelligence platform – click here for inquiry and access.
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Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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