What to Watch: On-Device Agents and Local LLM Boxes at CES 2027
Private inference is becoming a product category. On-device agents and local LLM boxes are becoming a real category. The private-inference hardware and software to watch at CES 2027.
The most interesting shift in consumer AI is not another chatbot, it is the move to run models locally, for privacy, latency, and control. (Analysis.)
Heading into CES 2027 we will track the hardware and software that make private, on-device inference practical: unified-memory desktop machines like NVIDIA's DGX Spark, mini PCs with capable NPUs and GPUs, and the local-model software stacks that have matured around tools such as Ollama and open web interfaces. The common thread is keeping inference off the public cloud.
How to read the demos (analysis): a slick on-device agent demo at a booth is easy; a dependable one that runs on hardware you can buy is the real test. We will be asking what model is running, on what memory, and whether it works without a network connection.
Forward-looking notes here are labeled as analysis, not confirmed announcements. We report only verified products, specs, and dates.
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