How AI PCs change the stakes for transforming enterprise architecture

Most organizations today operate with two primary compute tiers:

1. Centralized cloud and SaaS platforms
2. Client devices that consume them

AI PCs introduce meaningful inference and agentic capability directly at the client endpoint. Large language models and training workloads remain in the cloud, where centralization makes sense, while AI PCs handle distributed, task-specific models that benefit from speed, privacy or contextual awareness. Applications increasingly decide which tasks remain local and which are sent to the cloud.

The hybrid edge AI setup can increase speed, affordability and sustainability. Running models locally on an AI PC allows inference tasks to complete dramatically faster, more quietly, more affordably and with less resource use than their cloud-based equivalents. Enterprises can define new baselines for the performance, privacy, cost and energy efficiency of AI workloads.

Hybrid edge AI also provides enterprises more choice about where AI workloads belong. Instead of running models on either public cloud or on‑prem infrastructure, organizations can choose among multiple permutations of public models, local models and—increasingly—AI PC models using the devices employees already employ every day.

While testing AI PCs, we observed a clear performance and efficiency shift when running inference locally versus in the cloud. In one experiment conducted on Dell Pro AI PCs powered by Intel Core Ultra Processors, a locally run model delivered ~10x faster response times than the same query executed via cloud AI services, while achieving about 70% lower energy consumption and eliminating network dependency entirely. In our view, Dell platforms powered by Intel are well suited to hybrid edge AI use cases because they can distribute workloads across CPU, GPU, NPU and cloud resources.

This advanced functionality illustrates how hybrid edge AI allows enterprises to redefine baselines for performance, cost, privacy and sustainability by placing the right workloads at the endpoint while reserving the cloud for large-scale training and orchestration. Also, running AI inference locally reduces reliance on cloud‑based token consumption. When tasks are handled directly on the device, organizations avoid repeated API calls, network overhead and per‑use inference costs, thereby lowering operational spend while improving responsiveness.

Another advantage of hybrid edge AI is the potential for more secure handling of sensitive information. Running models and agents locally allows enterprises to process proprietary data or personally identifiable information without sending it to external systems. However, the shift to hybrid edge AI will require enterprises to find ways to distribute approved models to endpoints, apply updates consistently, enforce data masking and usage controls, and ensure security and policy compliance across thousands of devices. Governance of hybrid AI is going to demand more sophisticated observability controls than are needed for today’s agentic frameworks focused on cloud-based workloads. In a hybrid edge AI environment, governance does not move to the edge but extends to it.

A powerful yet less frequently discussed implication of local AI inference is the ability to operate in disconnected or intentionally isolated environments. Not every system can—or should—assume always-on connectivity. For example, government systems, defense environments and other sensitive workloads often operate on networks intentionally disconnected from the internet. In these contexts, cloud‑based AI is either impractical or prohibited.

For such organizations, the ability to run capable AI locally opens new opportunities, as AI can operate wherever work is done, regardless of connectivity. By enabling capable AI inference locally, AI PCs allow organizations to deploy intelligent workflows in air‑gapped environments, supporting analysis, decision assistance and automation without exposing sensitive data or relying on external connectivity.

The self-healing capability of AI PCs enables a better user experience for employees: fewer technical problems, less need for IT support and reduced downtime while solving issues.

Hybrid edge AI also opens the door to a more privacy-conscious approach to employee management, trading potentially intrusive behavior monitoring for modeling of each job’s requirements for tools and resources. The system supports the employee without requiring the tracking of their activities, an important distinction for both privacy and trust.