AI is getting expensive, and companies are looking to cut token costs. The solution: move more AI work to PCs.
The PC won’t replace the cloud. Preliminary AI work can be done on a PC, and tougher work — which today’s PCs can’t handle — can go to the cloud.
“We’ll always need to use the most advanced cloud-based frontier models for certain tasks, but… we don’t need to use them for everything,” wrote TECHnalysis Research Insights president Bob O’Donnell in a LinkedIn post.
That’s the vision Microsoft calls “hybrid intelligence,” in which Windows makes AI always available. The cloud models run the hardest tasks, and less demanding tasks run locally on the PC. For Windows machines, at least at this point, that means Copilot+ AI PCs.
“The future of AI is not about having just one model. It’s about having the right model at the right moment — frontier capability when you need it, smaller, faster models when you want speed,” said Pavan Davuluri, head of Windows and Devices at Microsoft, at an Oct. 7 PC launch event.
Hybrid intelligence will be everywhere, blurring the line between AI in the cloud and the PC, said Microsoft CEO Satya Nadella at the event.
“I just want to use my computing power wherever it is in sort of a seamless flow of what I’m trying to get done,” he said.
Agentic AI PCs
Microsoft recognizes that hefty cloud token bills are making enterprises hesitate to scale AI. Its answer: offload AI tasks that don’t need cloud models to AI PCs.
About 10% to 15% of AI workloads will run on PCs and 40% to 45% in the cloud in the next three to four years, said analyst Jack Gold of J. Gold Associates.
“Companies will need AI-accelerated PCs, and at some point we’ll likely see a renewed emphasis on buying newer machines,” Gold said.
Enter agentic AI PCs, which can run agents, generate video, write code and chat with advanced genAI models on a PC. If the PC can’t do the job, it is sent to the cloud.
“A year ago, such capabilities were exclusive to frontier cloud models. Now you can have these capabilities running on your PC,” Microsoft’s Davuluri said.
Agentic AI PCs are equipped with powerful GPUs that are distant cousins of the GPUs in data centers. Nvidia’s RTX Spark superchip — which has a Blackwell GPU — powers many of them, including Microsoft’s new Surface Laptop Ultra.
Unlike older AI PCs, the new agentic PCs can run full AI models and have enough compute power to run them fast, said Gerardo Delgado, senior director of product management at Nvidia. They give users and enterprises a path to advance AI projects.
“To us, an AI PC is the PC that will… run those local models so that you can actually keep the level of intelligence and enjoy the economic benefits,” Delgado said.
Local, by design
HP’s newly announced ZBook Ultra G3a mobile workstation has an AMD GPU that generates free tokens and cuts cloud bills, said Brian Allen, manager for Global Z workstation products at HP.
“It can be on-prem. They don’t have to worry about confidential information going to cloud, or tracking tokens and getting $1,000 charges a week,” Allen said.
The ZBook Ultra G3a packs Perplexity’s Portable Computer service, which has agents that can link a local model to frontier models in the cloud.
For example, an agentic PC can handle basic AI-based design work in Autodesk’s Revit 3D modeling software without an internet connection. For more advanced design work, Perplexity can advance it to one of 19 frontier models in the cloud, whichever fits best.
Designers will likely offload general tasks to frontier models in the cloud and keep proprietary work local, Allen said.
HP links to Perplexity’s tools in the Windows taskbar. Perplexity has MCP connectors for Revit, and users can just type in their desire.
“I can do contextual typing of, ‘hey, build this for me,’ without having to know the details of that,” Allen said. It takes years to learn the intricacies of Revit, but “I can really learn how to do Revit through agentic AI in a matter of hours,” he said.
Coding pest control
Microsoft demonstrated how a local model could save token costs typically incurred in the cloud.
A team couldn’t reproduce a software bug, so GitHub Copilot picked GPT-6 Luna in the cloud to analyze the code. The cloud model then handed off building and testing to local sub-agents running MAI Code 1.1 Flash on a PC to reproduce the bug.
The local work was free. One sub-agent fed 1.6 million tokens of code and context to the local model, which generated 10,500 tokens in response, a GitHub product manager said during the demonstration.
To make that possible, Microsoft compressed the 130-billion-parameter MAI Code 1.1 Flash model so it can run on a PC.
“We have taken that model to three bits of precision, reducing its size by nearly 80% while preserving coding quality,” Davuluri said.
The demonstration used GitHub’s HydraFusion feature, which moves coding tasks between local models and the cloud. Microsoft is bringing the feature to GitHub’s Copilot app for Windows Copilot+ PCs.
Many challenges remain
Using agentic AI on PCs isn’t as simple as installing models or software, said analyst Gold. Companies will also need to develop and customize models for such PCs, depending on applications in enterprise environments.
Open-source AI models, which can be customized, on those machines could “significantly reduce AI costs and increase ROI,” Gold said.
Also, the first agentic AI PCs hitting the market are expensive. Microsoft’s Surface Laptop Ultra starts at around $2,600, reaching $6,000 with all the bells and whistles.
A cheaper laptop can do the same job using cloud AI models, and the Surface could quickly go out of date as new models arrive, wrote senior reporter Mark Hachman at sister publication PCWorld in a product review.
Analysts expect PC prices to remain elevated until the end of 2027, partially due to high component costs, the Iran war, and an uncertain economic environment.
A hybrid AI environment is the way forward, but using AI on a PC or the cloud isn’t exactly seamless yet, wrote Tom Mainelli, group vice president at IDC, in a LinkedIn post.
“For much of this to waterfall down to the average user, many different things will need to happen across component pricing, model evolution, agentic use cases, and more,” Mainelli wrote.
HP is still trying to figure out a market for agentic AI PCs. It’s a long learning curve for customers on how to use it, Allen said.
“They’re in the ‘this PC is too good to be true’ phase… ‘let me benchmark it on my own RAG model and see how it really works,'” Allen said.