Local AI runs on the computer a person is using, without sending the work to a cloud service. Microsoft and NVIDIA have put it on the PC roadmap in unusually concrete form. At its Build 2026 developer conference, Microsoft highlighted the Surface RTX Spark Dev Box, a computer for developers doing sustained AI work. Developers can use it to customize and run large AI models on the machine, without renting computing power in the cloud.
NVIDIA describes RTX Spark as a superchip with up to 128GB of memory. AI model size is counted in parameters, and the chip can run models with 120 billion of them on the machine itself. On its Windows blog, Microsoft said RTX Spark will power laptops and small desktops from Surface, ASUS, Dell, HP, Lenovo and MSI beginning in autumn 2026.
Local AI has existed for years. People already run models on personal machines with tools such as LM Studio and Ollama. What is new is the commercial packaging, with better hardware, operating system support and familiar developer tools lined up behind it. GitHub, for one, says its Copilot CLI coding tool can connect to local models and run offline in restricted environments.
Privacy is the first business reason. When an employee sends sensitive text, code or documents to a cloud model, that information leaves the company. Running the model on the device reduces that movement. LM Studio says downloaded models can be used offline, and that chats and documents stay on the machine. Ollama says it does not see people’s requests or data when it runs locally.
Cost is the second. Cloud AI is billed by use, so the bill grows with repeated use across teams, documents and coding sessions. OpenAI’s pricing page tells customers to monitor spend and set monthly budgets. Local models give companies another place to send work when the strongest cloud model is not needed.
Every machine has limits. Local models still trail the leading cloud systems on raw capability, especially on complex tasks that need strong reasoning or a lot of background material. That turns local AI into a workload choice. Some tasks deserve the best outside model. Many routine drafting, search, coding and internal assistance tasks may be good enough on a local model, especially when privacy or cost is the constraint.
