The AI Factory: A strategic overview with Hewlett Packard Enterprise

Many organizations find that their AI initiatives, despite early promise, fail to deliver a positive ROI. This can be traced to “token economics”—the complex and often unpredictable costs associated with consuming AI models, particularly in the public cloud. This session will dissect these hidden costs and the architectural bottlenecks that lead to runaway spending and stalled projects. We’ll then present a comprehensive overview of HPE Private Cloud AI, a full-stack, turnkey solution designed to provide predictable costs, superior performance, and total control. We will explore how its integrated hardware and software—from NVIDIA GPUs and HPE servers to a unified management console—enable a powerful and predictable path to production, turning AI from a financial gamble into a strategic business asset.

The presentation highlights the often-overlooked costs associated with AI initiatives in the public cloud, citing examples like over-provisioning, lack of checkpointing, and inefficient data usage. The speaker emphasizes that many companies experience significantly higher operational costs than initially anticipated, with one example of an oil and gas company spending ten times more than projected. While some companies may not be overly concerned with these cost overruns if the AI models deliver results, HPE contends that this isn’t sustainable for most organizations and that there are cost savings to be found.

HPE’s solution, Private Cloud AI, offers a predictable cost model and significant savings compared to cloud-based alternatives. These cost savings, averaging around 45%, are most pronounced with larger systems managed within the customer’s own data center, though co-location options are also available with slightly higher overhead. Furthermore, HPE’s solution addresses the hidden costs associated with building and managing an AI infrastructure from scratch, including the need for specialized teams and resources for each layer of the technology stack.

Beyond cost considerations, HPE’s Private Cloud AI provides greater control over data, mitigating concerns about data privacy and usage in downstream training cycles, which is important considering inquiries into the training data used for some AI models. The solution offers flexible purchasing options, including both CapEx and OpEx models, with HPE GreenLake enabling reserved capacity and on-demand access to additional resources without upfront costs. This combination of cost-effectiveness, control, and flexibility positions HPE Private Cloud AI as a compelling alternative to the public cloud for AI deployments.