The AI boom is rippling through digital infrastructure, and many data centers are unprepared. AI has already become a larger percentage of data center workloads, with training and fine-tuning large language models, inferencing, and high-density workloads taking up nearly 20% of capacity. However, at least two times the amount of new data center capacity is needed than exists currently.

The size and complexity of AI workloads is also increasing dramatically, as breakthroughs related to generative AI and large language models (LLMs) require even more computing resources than traditional AI. Newer generative AI models are pre-trained on enormous amounts of data — 45 terabytes in the case of OpenAI’s GPT-3 model — which requires incredibly powerful hardware and supporting infrastructure.

Organizations will need to optimize their current infrastructure but also prepare for the future demands of AI as it continues to evolve. Solutions that offer power, scalability, and flexibility for AI compute, storage, and software-defined infrastructure will win the day.

In this guide, AHEAD explores the impact that AI workloads are having on traditional data centers, and strategies to modernize infrastructure for AI.