In an era where AI is reshaping our lives and businesses, the importance of a robust tech infrastructure can't be overstated. Companies are using AI technologies to streamline operations, improve customer experiences, and generate valuable insights. However, implementing AI into your business is not a walk in the park; it requires a solid technological foundation. This article explores how businesses can build this foundation and thrive in the AI era.

Generative AI, a subset of AI that harnesses deep learning, is gaining popularity due to advancements in IT infrastructure, particularly storage. Storage tools such as object storage and distributed parallel file systems are vital for supporting generative AI by providing fast access to large datasets and allowing for high-performance data processing.

Large language models (LLMs) like GPT-3 require billions of nodes, making storage even more critical than keeping everything in memory. While smaller AI models can be executed in memory, the cost of RAM is higher than storage. High-throughput storage is needed to handle the reading and writing of millions of images in generative AI.

GPUs play a critical role in generative AI by accelerating model training. However, when working with millions of images, GPU buffers can quickly fill up, and images need to be written quickly to storage. High-throughput storage helps reduce data bottlenecks.

While flash storage is not necessary for generative AI, it provides high IOPS in denser footprints and can offer aggregated performance for LLMs. Flash storage is also denser, higher performing, and uses less power than HDDs, making it optimal for generative AI.

The choice between cloud and on-premises storage for LLMs depends on factors such as the model size, performance requirements, and data control. Cloud storage is ideal for research purposes, as it offers scalability without the need for upfront infrastructure investment. However, for enterprises with production workloads and specific performance and security needs, on-premises storage is key.

Remarkable storage solutions like Pure Storage's FlashBlade, Dell PowerMax, and Vast Data's Universal Storage are suitable for supporting LLMs. These high-performance scalable storage systems are being used by successful businesses, like Crater Labs, to support their generative AI work.

Building a next-generation storage foundation for AI workloads requires a solid storage infrastructure that can handle growing datasets and demanding workloads.

IBM's WatsonX is an AI and data platform that allows enterprises to scale and accelerate the impact of AI throughout their business. WatsonX.ai allows businesses to train, validate, tune, and deploy AI models at scale across their enterprise. WatsonX.data offers built-in governance and automation, providing trusted insights within minutes.

WatsonX is designed to empower enterprises to effectively leverage AI and data to drive business value and achieve their digital transformation goals.

Security is a crucial consideration when setting up an AI environment. Solutions like Dell's Cyber Protection provide robust safeguards for your data and infrastructure. Leveraging these measures is vital for preserving the integrity of your AI models, protecting your business from potential threats.

To stay competitive in the AI era, businesses must invest in building a resilient tech infrastructure. It requires not only a strong storage solution but also powerful processing capabilities, robust security measures, and strategic decisions between on-site and cloud storage.

By making these strategic investments, businesses can position themselves for success, fully harnessing the power of AI to drive growth, innovation, and lasting competitive advantage.