CES 2026 emphasized private AI momentum, with exhibitors across the board showcasing edge hardware, enterprise rack solutions, and AI PC chips. This signals that localized, secure AI computing is moving towards mainstream deployment.
The shift toward private compute for AI enables organizations to bring critical workloads and intelligence on-premises, thus enhancing security, compliance and real-time performance over reliance on the public cloud.
Advancements in edge infrastructure, high-performance hardware and the combination of IT and OT are enabling powerful local computing, cost savings and new efficiencies across industries

Democratizing Local High-Performance Computing

At CES, major OEMs showcased how professionals can handle complex work directly on their own computers, keeping sensitive information safe while boosting productivity and efficiency. According to Counterpoint Research’s Global AI Consumer Spends Forecast 2024-2030, the AI hardware market will account for most of the spending, driven by the integration of AI capabilities into personal devices. The hardware needed to run advanced programs is now more accessible than ever before. AI PCs are quickly becoming standard for today’s workforce. Workers are using these powerful computers to perform tasks such as data analysis and secure and productive decision-making.

Qualcomm highlighted top-performing chips at CES, pushing performance to 80 TOPS for AI PCs. This enables local processing for use cases such as Outlook thread summarization and real-time noise cancellation without taxing the CPU or GPU. It also ensures that sensitive data, such as corporate email contents or datasets, never leaves the local device. More on Qualcomm’s CES 2026 updates and Counterpoint’s AI PC coverage can be found here.

Siemens is integrating NVIDIA’s Nemotron open AI models into its electronic design automation (EDA) software. By prioritizing domain specialization over generic model capabilities, Siemens aims to maximize accuracy in complex engineering workflows and significantly lower operating costs, demonstrating that specialized open-source architectures can outperform generalist models in high-pressure industrial environments. Targeted models for specific use cases will be key to optimized performance. General models are frequently inefficient for specific manufacturing requirements. By fine-tuning open-source models for specific tasks, edge-native solutions are faster and more secure than querying centralized LLMs.

Enterprise Specific Private Compute

At CES, there was a common trend of bringing computing power closer to the edge. Enterprises want control and don’t want to risk leaking confidential information or competitive advantage. Cost, latency and privacy are driving CIOs to plan for edge computing strategies. Large-scale examples include Goodyear, which built on-campus data centers to move specific operations on-premise.

Strata paneled at CES, introducing individual racks for enterprises, enabling a secure AI infrastructure customized to each company. This allows enterprises to "bring the AI to the data," preserving compliance and security for sensitive industries like pharmaceuticals and aerospace.

The integration of IT and Operational Technology (OT) is unlocking major efficiencies within organizations. By using local AI to correlate and analyze data from HVAC, lighting and security systems, companies can make smarter, real-time decisions that significantly cut operational power consumption. These operational enhancements not only lower energy usage but also translate into considerable cost savings.

At CES 2026, we saw how private AI compute is transforming everything from manufacturing to connected devices. Computational power is being moved on-premises and on-device to secure sensitive data and improve workflows. Enterprises and individuals may still utilize public cloud services in some cases, but enterprises need to secure sensitive internal data, which is driving organizations to private AI computing. By moving critical workloads back to on-premise environments, organizations can preserve data security with lower latency.

The Network Matters

Private 5G and Wi-Fi 7 are reducing latency to levels required for fast decisions and even real-time inference. At the same time, software-defined networking (SDN) gives organizations the control to prioritize AI traffic, protect performance and maintain consistent service quality as the network becomes more complex. High-bandwidth, low-latency networking is the glue that makes private AI compute work in the real world. These edge deployments are distributed across factories, hospitals and offices, all of which need to stay in sync. That means reliable connectivity between sensors on a factory floor to local inference boxes is crucial to support edge clusters.

Implementation With Human in the Loop

Many of the AI-themed panels at CES emphasized that systems must be engineered with human-in-the-loop governance, especially for high-risk decisions such as hiring, acquisitions, or operational changes. These are areas where AI still struggles to make appropriate contextual assumptions. Key principles such as transparency, contestability and oversight should guide the deployment of AI solutions, providing a framework for responsibly managing AI systems. It is important to ensure that humans can oversee model reasoning and intervene, especially when it comes to decision-making activities.

United Airlines’ apprenticeship programs, highlighted at CES, show how AI can drive workforce transformation by training individuals from non-traditional backgrounds for advanced technology roles. To successfully manage the AI era, organizations and individuals must begin preparing now. Like United Airlines, putting systems in place to acclimate to new skill requirements and prepare for the future AI will hold.

Outlook

At CES 2026, we saw a shift from cloud-first thinking to organizations seeking ways to take control over their data, security and operations. This shift isn’t about abandoning the cloud; it’s about making more intelligent choices over what goes to the cloud. The private AI movement will be a long-term global trend. Counterpoint is anticipating significant spending on AI hardware, both at the enterprise and consumer levels. Companies are finding real value in linking advanced local hardware with application-specific AI models, enabling everything from real-time judgment to energy savings on the factory floor.

Companies that succeed in the private AI world will have robust local infrastructure, clean data and integrated IT/OT systems. Organizations that place importance on transparency, upskilling and joint efforts between IT and OT teams will be better equipped to overcome the challenges ahead. Localized operational excellence is becoming increasingly important for enterprises pursuing security and low latency.

The Rise of Private Compute at CES 2026

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