Microsoft Unveils Windows AI Foundry: A New Era for AI Development on PCs

AI dev tools for Windows get a fresh coat of paint | TechCrunch

Microsoft wants to make it easier for developers to build AI-powered apps on Windows devices. On Monday during its Build 2025 conference, Microsoft announced Windows AI Foundry, a rebranding and expansion of the Windows Copilot Runtime service the company launched last May. Microsoft describes Windows AI Foundry as a "unified platform for local AI development" -- a way to fine-tune, optimize, and deploy the AI models underpinning Windows apps. Microsoft increasingly sees AI, whether running locally or in the cloud, as a major profit driver, despite the high costs associated with developing tentpole models. In January, Microsoft CEO Satya Nadella said the tech giant has $13 billion in annualized AI revenue, in part thanks to its partnership with ChatGPT maker OpenAI. Microsoft might not profit directly from third-party AI-powered apps on Windows. Still, by providing resources to make it easier to build AI software on the tech giant's flagship operating system, Microsoft aims to foster a flourishing ecosystem at the expense of rivals. Hence, Windows AI Foundry. According to a press release shared with TechCrunch, Windows AI Foundry can automatically detect a Windows machine's hardware and fetch the necessary software components to run a particular AI model. Windows AI Foundry will also keep these components up to date as new devices are released, Microsoft says, and deliver tools designed to simplify the process of prepping models. Windows AI Foundry also includes Foundry Local, a new service that "bring[s] the power of [AI] models [...] to client devices," explains Microsoft in the press release. Foundry Local, which supports macOS in addition to Windows, comes with a set of modules to run AI models and tools directly on-device, leveraging the ONXX Runtime, an open source project aimed at accelerating AI across platforms. Foundry Local also ships with command-line interface support, letting developers use prompts like "Foundry model list, Foundry model run" to browse, test, and interact with models running on a local server. "Foundry Local will automatically detect device hardware -- CPU, GPU, and NPU -- and list compatible models for developers to try," Microsoft writes. "Developers will be able to leverage the Foundry Local SDK to easily integrate Foundry Local in their app."

Microsoft unveils Windows AI Foundry for AI-powered PC apps

Microsoft is replacing 'Copilot Runtime' with Windows AI Foundry to help developers build, experiment, and reach users with AI experiences in their apps. Windows AI Foundry will not only include Microsoft's model but other open-source models as well, including those from Nvidia, such as NVIDIA NIM. With these models, developers can integrate new AI experiences and models in their applications. "In addition, Windows AI Foundry offers ready-to-use AI APIs that are powered by Windows inbox models on Copilot+ PCs for key language and vision tasks, like text intelligence, image description, text recognition, custom prompt and object erase," Microsoft announced in a blog post. "We are announcing new capabilities like LoRA (low-rank-adaption) for finetuning our inbox SLM, Phi Silica, with custom data. We are also announcing new APIs for semantic search and knowledge retrieval so developers can build natural language search and RAG (retrieval-augmented generation) scenarios in their apps with their custom data." AI Foundry in Windows also makes it easier for developers to use powerful AI models on their PCs without needing a lot of setup or optimization. It checks what kind of hardware your computer has and then shows which AI models will work best. For example, AI Foundry automatically matches models to your device's hardware, so if you're a developer, you don't have to figure it out. Here is the complete list of features: All of these features will soon be built directly into Windows 11 and the Windows App SDK.

Microsoft unveils Windows AI Foundry, retools PC operating system for agents

Microsoft is taking the next step in its journey to put Windows back at the center of computing. Windows AI Foundry, announced at the company's Build developer conference Monday morning, is a new framework to help developers fine-tune and run AI models directly on PCs. It's part of a broader effort by the company to reposition Windows as a platform for AI, in much the same way the its flagship PC operating system helped to usher in the early days of the web. Windows AI Foundry includes tools for deploying open-source and custom models on local hardware, including CPUs, GPUs, and NPUs (neural processing units) in Copilot+ PCs. The shift toward running advanced AI locally on devices is happening faster than the company expected, said Stevie Bathiche, Microsoft technical fellow and head of the company's Applied Sciences Lab, during a briefing with reporters Sunday on the company's Redmond campus. Windows AI Foundry uses "all the processors that are there, because it's not just the neural processing unit," Bathiche said. It's "the CPU, the GPU and the NPU. Managing the workloads efficiently across all the devices is super important." While this change doesn't eliminate the need for cloud-based AI, he said the ability to bring those capabilities to the client side marks a major turning point -- part of a larger transition toward AI agents as a new foundation for software development and interaction. Bathiche described the shift as a major change in how people use computers. For decades, it has been all about the keyboard and mouse. Now, he said, AI and AI agents are changing how people interact with software. "The North Star that we're all headed to is really the agent," Bathiche said, describing the AI agent as "the new unit of interaction" and a "new unit of programming." Copilot+ PCs are new Windows laptops and desktops built to handle AI tasks directly on the device. They are designed to run AI features quickly and efficiently without needing to connect to the cloud. Microsoft says it expects that within a few years, most new Windows machines will fall into this category, making advanced AI tools an everyday part of the PC experience. The main focus of Microsoft Build this week is the company's evolution from standalone chatbots and copilots to a broader vision it calls the "open agentic web" -- in which autonomous agents act on behalf of users and interact with one another. PREVIOUSLY: What AI can learn from the browser wars: Microsoft CTO calls for open standards to fuel 'agentic web'

Microsoft debuts Windows AI Foundry for local model development on AI PCs - SiliconANGLE

Microsoft debuts Windows AI Foundry for local model development on AI PCs Microsoft Corp. said today it's advancing the local artificial intelligence development capabilities of Windows, as part of an effort to help developers build and experiment and reach new users with sophisticated AI experiences. At Microsoft Build 2025 running this week in Seattle, the company said it has evolved Windows Copilot Runtime into a new service called Windows AI Foundry, offering numerous powerful new features to enable AI integrations in applications. The service integrates large language models from Foundry Local and other model catalogs, such as Ollama and Nvidia NIMs, providing developers with easy access to a range of ready-to-use open-source models. They're optimized across multiple types of hardware so they can be instantly deployed, the company said. According to Microsoft, Foundry Local will instantly detect the hardware developers are using -- be it a central processing unit, graphics processing unit or neural processing unit, and list all of the models that are compatible with that chipset. Then, they can use the Foundry Local SDK to integrate Foundry Local directly within their applications. In addition, Windows AI Foundry will also support the large number of developers who are building their own LLMs. It said Windows ML will act as the built-in AI inference runtime to streamline model deployment across every CPU, GPU and NPU. Windows ML is a high-performance local runtime that's built directly into the Windows operating system, in order to simplify the task of shipping production applications or proprietary models, including Microsoft's own Copilot+ PC experiences. It represents an evolution of the DirectML runtime, and incorporates feedback from silicon partners including Intel Corp., Advanced Micro Devices Inc., Nvidia Corp. and Qualcomm Inc. Microsoft's corporate vice president of Windows + Devices Pavan Davuluri said in a blog post that Windows ML provides a number of benefits to developers, beginning with simplified application deployment. He said developers will be able to ship production applications without needing to package ML runtimes, drivers or hardware execution partners with their apps. Instead, Windows ML just detects the hardware on the client's devices and chooses the most appropriate execution provider, based on the app's configuration. Davuluri added that Windows ML can also adapt automatically to new AI hardware. So as new CPUs and GPUs become available, it will keep all of the required dependencies up to date and adapt to the new silicon, ensuring model accuracy while maintaining full compatibility with the underlying hardware. Finally, Windows ML will come with a range of tools included within the AI Toolkit for VS Code to simplify tasks such as model conversion, model quantization and model optimization, all in one place. Davuluri stressed that Microsoft has worked very closely with its silicon partners to integrate their third-party execution providers seamlessly with Windows ML, providing the best model performance for whatever hardware is available on the developer's machine. To simplify key AI tasks such as text intelligence and image processing within applications, Microsoft has created a number of ready-to-use application programming interfaces. These include text summarization, rewrite and vision APIs such as text recognition, image description and image super resolution, available now in the Windows App SDK 1.7.2. According to Davuluri, the new APIs are meant to eliminate the overheads associated with model building and development. The APIs run locally on developer's devices, ensuring compliance, privacy and security, and they're fully optimized for NPUs on Copilot+. In addition, Microsoft is looking to cater to developers who need to fine-tune open-source LLMs with custom data with the launch of LoRA support for Phi Silica. LoRA (low-rank-adoption) for Phi Silica is in public preview now on Snapdragon X Series NPUs. It's aimed at making model fine-tuning more efficient. It does that because it only updates a small subset of parameters of each model using the developer's customized data. That can help to increase the performance of models on a specific task without affecting its broader capabilities, Davuluri said. To get started, developers will be able to access LoRA training training for Phi Silica in the AI Toolkit for VS Code. It can be found in the Fine Tuning Tool menu. Developers select the Phi Silica model, configure the project and immediately kick off the training on Azure using the custom data set. Once that's complete, developers can download the LoRA adapter and use this atop of the Phi Silica API and start experimenting to see how different its responses are. Elsewhere, Microsoft announced a set of new Semantic Search APIs for developers to create more powerful search experiences in their applications leveraging their own data. The APIs enable both semantic and lexical search, allowing users to search by both meaning and their exact words, making it easier for them to find exactly what they need. The search APIs will run locally on all device types, Davuluri said. Beyond traditional search, they also support retrieval-augmented generation, giving developers a simple way to ground their model's outputs in their own custom data. They're available now in private preview on all Copilot+ PCs, Microsoft said. For developers who prefer to build their applications directly in the cloud, the Azure AI Foundry is getting plenty of updates too. Azure AI Foundry is a comprehensive platform for designing, building, customizing and managing AI applications and agents. It provides everything from models and agents to development tools and observability within a single portal. With the latest version of Azure AI Foundry, Microsoft said it's streamlining model selection, customization and monitoring. In addition, the Foundry Agent Service has become generally available, paving the way for teams to customize, deploy and run multiagent apps at scale. To make model choice easier, Microsoft has created a new model leaderboard that ranks hundreds of LLMs based on their quality, cost and throughput. It has introduced a smart model router that can automatically select the most appropriate model for each request, based on the developers latency and budget constraints. According to Microsoft's high-performance computing engineer Yina Arenas, early adapters saw cost savings of up to 60% by using it to optimize model selection. The platform's fine-tuning tools have been upgraded too, and developers can now fine-tune models with reinforcement techniques such as GPT4.1nano, o4mini and Llama 4 in Foundry Models. There's also a new, low-cost Developer Tier available, which eliminates hosting fees during experimentation. As for Foundry Agent Service, this is now generally available, making it simple for developers to host a single AI agent or groups of them, and expose them using the agent-to-agent protocol. Arenas said the service is meant to simplify AI agent development and deployment, integrating with data sources such as Microsoft Bing, SharePoint, Azure AI Search and Microsoft Fabric, while supporting task automation via tools like Azure Logic Apps and Azure Functions, plus third-party tools that use the open-source Model Context Protocol. Other new capabilities include a new software development kit that merges the old Semantic Kernel and AutoGen SDKs, creating a single, composable API for defining and deploying AI agents with identical behavior, locally or in the cloud.