When you think of AI, you might think of gigantic data centers and cloud connections. Remote GPUs, computing power behind an API, and data traffic throughout the network. But that image has changed over the past year. Thanks to laptops with powerful NPUs, more and more hybrid AI is happening right on the device: transcriptions, summaries, image corrections, even real-time translation. Not in the cloud, but on your own machine. And this is not a hype – it is a strategic shift.
In Baaz Magazine: All About AI, we explore the practice of this local, hybrid AI: why it pays off to perform smart tasks closer to the user right now, what that means for latency, security, TCO, and energy consumption, and how you can measurably determine which tasks belong locally or which are better left in the data center.
Hybrid AI: the right work in the right place
We will guide you step by step: from the differences between CPU, GPU, and NPU to a practical buying compass with concrete hardware choices per user profile. You will learn how to link realistic expectations to TOPS values, why not every NPU is equally usable, and how to prevent an AI sticker on the box from leading to disappointment in practice.
What can you run locally – and what not? What do ARM chips, Core ML, ONNX, and quantization mean for your workplace? And how do you maintain control over security, governance, and updates as AI also starts making independent decisions on the laptop?
From Copilot to custom models, from efficiency to safety: With our decision tree, a 90-day adoption plan, governance checklists, and an overview of AI-ready devices, we ensure that you can get started fully prepared.
For IT departments, 2025 is the moment to streamline policies and hardware: Windows 10 is coming to an end, NPUs are reaching maturity, and the first security guidelines for on-device AI are available. Those who measure, test, and organize now will save on cloud usage, prevent data leaks, and accelerate processes with visible impact.
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