Around the corner: Agentic AI PCs that cut token costs
AI PCs that cut token costs? That may appeal to enterprises. A new breed of agentic AI PCs promises to do exactly that. The powerful laptops can cut token costs by completing AI work locally instead of sending it to expensive LLMs in the cloud. HP’s newly announced ZBook Ultra G3a mobile workstation and upcoming laptops from other PC makers have powerful GPUs and large memory pools designed for…
Agentic AI PCs could slash costs, say industry experts. These new laptops, set to hit the market soon, leverage powerful GPUs and large memory pools to run AI workloads locally, rather than relying on expensive cloud-based large language models (LLMs). HP's upcoming ZBook Ultra G3a and other PC makers' models will be powered by AMD's Ryzen AI Max Pro processors and Nvidia's RTX Spark superchips.
Nvidia's GPUs have long dominated data centers, but the company is now targeting the PC market with RTX Spark, which could drive a significant shift in AI workloads from the cloud to local hardware.
AI PCs can perform tasks like video generation, code writing, and running AI models with billions of parameters without an internet connection. This local processing could help enterprises cut recurring cloud costs, which have become a concern for many organizations. While cloud-based AI models are more powerful, these AI PCs can provide a cost-effective alternative for high-end enterprise workloads.
Analysts predict that within two to three years, 20%-25% of high-end AI workloads will run on AI PCs instead of solely in the cloud.
However, AI PCs won't be inexpensive, but they will offset the cost of cloud-based AI processing. HP hasn't disclosed pricing for the mobile workstation, but it is expected to be more expensive than regular AI PCs like Microsoft's Surface Laptop 13-inch and Surface Pro 12-inch models. Nonetheless, HP offers an ROI calculator to help potential buyers evaluate the cost savings of running AI locally versus in the cloud.
While AI PCs offer significant advantages, they are not a one-size-fits-all solution. Enterprises will likely adopt a hybrid approach, utilizing AI PCs, edge devices, and cloud-based models depending on the specific workload requirements. The key to success lies not just in having more powerful hardware but also in having skilled AI engineers who can effectively leverage the technology.
Written by urgent.news from Computerworld's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
