Unlocking Local AI: How Existing PC Fleets Can Support Enterprise AI

Cloud AI will continue to play an important role, particularly for workloads requiring elastic scale or access to highly capable models. At the same time, sending every workload to the cloud can raise questions about cost, sensitive data, intellectual property, latency, and control. Current enterprise discussions increasingly position local and cloud AI as complementary parts of a hybrid architecture, with workload placement shaped by data sensitivity, latency, scale, and cost.
This creates a new opportunity for organizations with AI PCs already deployed or entering their refresh cycles. Rather than treating each PC solely as an individual productivity device, organizations can consider how available compute and memory across the fleet might support private AI workloads within the enterprise environment.
This interactive roundtable will explore how organizations can maximise the available compute across their existing PC fleets to support more local AI workloads and determine the right balance between endpoint, distributed, and cloud-based AI. Participants will discuss key considerations including data privacy, governance, cost predictability, workload placement, and the practical steps needed to turn existing PC fleets into a more active enterprise AI resource.
Join Intel and Cascadia for a peer discussion on how to unlock the potential of existing enterprise PC fleets to support local, private AI. Explore how local AI can complement cloud AI, which workloads are best suited to run closer to the enterprise, and what IT leaders should consider when building infrastructure for AI at scale.

Since 2022, Cascadia has built distributed software that makes many machines work as one to provide private AI infrastructure for the computers enterprises already own. Co-developed with Intel, their systems teach smaller, local machines to think together so companies can run powerful frontier AI models locally without depending on an external data center.