The rise of generative AI has ignited a demand for Graphics Processing Units (GPUs), with businesses rushing to leverage their power for advanced workloads. However, this rapid adoption presents a unique challenge: how can organisations ensure their investments in GPU-supported infrastructure deliver value in a rapidly evolving landscape?
Dave Link, CEO and co-founder of ScienceLogic, explores the key considerations for managing GPU performance and costs, highlighting the critical role of monitoring and strategic deployment.
The Importance of GPUs in the Age of AI
The global GPU market is booming, projected to reach £52 billion by the end of the decade. This surge is driven by the increasing demand for GPUs in AI applications, particularly in training large language models (LLMs), processing complex datasets, and powering demanding tasks like digital twins and metaverse applications. For businesses striving to harness the power of AI, GPUs are the essential engine, providing the computational muscle needed to deliver cutting-edge solutions.
However, this growth comes with cost implications. The high price of GPU hardware and their energy consumption demand careful planning and management. CIOs and CFOs must strike a delicate balance between processing power and expenditure, ensuring that GPU investments deliver a return on investment.
Improving GPU Monitoring: Visibility and Control
The adage "you can't manage what you can't measure" applies strongly to GPUs. Understanding and optimising GPU performance requires comprehensive monitoring, providing visibility into their utilisation and operational health. This involves gaining insights into query volume, user access patterns, and dataset training attributes, allowing for fine-tuning of utilisation rates.
Ideally, GPU monitoring should be accessible to analysts, presenting GPU processes within the context of the business activities they support. This "service-centric" approach offers a more holistic view of GPU performance, revealing their impact on business operations. Additionally, monitoring solutions should be vendor-agnostic, considering the diverse and evolving IT environments of most enterprises.
Effective GPU monitoring goes beyond hardware, encompassing the health of the software running on them. This includes tracking the performance of LLMs, inference engines, and other AI tools, providing a comprehensive view of the entire AI ecosystem. By understanding the efficiency of GPUs and their impact on business processes, IT leaders can make informed decisions on their deployment and optimisation.
Expanding Options: Cloud vs. On-Premise Hosting
The choice between cloud and on-premise hosting for GPU workloads is a crucial one, often overlooked during cloud migrations. While cloud-based GPU servers offer convenience, they can be limited in terms of cost, capabilities, and configuration options, and raise concerns around security and data sovereignty.
This has led to a growing trend of GPU repatriation, bringing infrastructure or applications back on-premises. This shift aims to reduce costs, enhance value, and address underutilised cloud migrations. Additionally, the rise of private AI, where companies keep data and proprietary AI modelling on-premises, further fuels the on-premise deployment of GPUs.
The Bottom Line
GPUs are essential for businesses harnessing the power of AI, and effectively managing their performance and costs is crucial for success. By implementing comprehensive monitoring and considering the full spectrum of cloud and on-premise options, IT leaders can ensure their GPU investments deliver optimal value and support the ever-evolving demands of the AI landscape.
