Large language models (LLMs) are demanding significant updates to IT infrastructure that many enterprises struggle to provide. Key challenges include high-density GPU workloads, massive data flows, and the need for high-speed networking. Patrick Ward, senior director at Penguin Solutions, emphasizes that managing LLMs requires both strong computational capacity and an architecture that can adapt to unpredictable performance demands. Organizations risk facing hidden costs such as latency issues and underutilized GPU resources if they do not adapt their infrastructure effectively.
To meet these challenges, IT leaders should perform a multi-level AI readiness assessment, starting with an evaluation of their current technology setup. This includes ensuring compute, network, and storage elements function cohesively and understanding how they will scale in response to anticipated AI growth. Additionally, evaluating workforce skills and establishing a thorough AI governance strategy are crucial. Compliance with fast-evolving regulations and ethical standards can safeguard enterprises from operational risks. Finally, benchmarking against industry standards allows organizations to identify gaps and streamline their AI implementation processes.
👉 Pročitaj original: CIO Magazine
