- AI PCs are emerging as a viable option to run local AI without unpredictable costs
- One-time PC cost alleviates need to shell out cloud token fees
- Wider research reaffirms growing popularity of smaller models
New data from Gartner claims that now could be a good time to buy AI-enabled PCs, as cloud computing faces numerous challenges in a rapidly changing business world.
Data center construction is lagging behind demand as supply chains become strained and local communities oppose new projects, meaning metered computing could end up costing some companies more than they expected.
By moving some of their AI processing on-premises, businesses could avoid some of those additional monthly costs with the one-time purchase of a more powerful PC as part of their regular upgrade cycles.
AI PCs present an ideal hybrid computing model
While adoption of AI PCs started out quite slow and businesses struggled to understand the benefits, they are now seen as an alternative cloud option rather than a primary benefit in their own right.
As the use of AI becomes more pronounced and unpredictable token consumption hits businesses hard, forecasting monthly costs is a major new challenge many are facing.
Small language and reasoning models, including models specially trained for individual business use cases, ultimately require fewer resources than leading frontier models, allowing them to run locally as part of a broader hybrid approach.
Gartner predicts that voice and chat, text generation, image and audio generation, and more could soon move to workers’ PCs, with only the most intensive tasks routed through hyperscaler data centers.
By 2029, the company’s researchers anticipate that around a third (30%) of companies could use AI-enabled PCs to reduce the costs of AI tokens in the cloud. By 2030, 70% of corporate PCs could run some GenAI tasks locally.
Omdia researchers also noted a shift in AI model usage, with smaller and medium-sized models proving popular, with domain-specific tasks that do not require the full breadth of computing.
“Older GPUs retain value and remain in service as they continue to offer a cost-effective option for small and medium-sized model inference and disaggregation,” said Advanced Computing Senior Principal Analyst Alexander Harrowell.
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