Local AI on Snapdragon Notebooks
Windows notebooks powered by Qualcomm’s Snapdragon X SoCs are more than just energy-efficient ARM-based machines. Alongside the CPU cores (named Oryon), they feature a built-in Adreno GPU and a dedicated neural processing unit (NPU). This hardware can boost AI models running locally—provided the software is optimised for ARM64 and the Snapdragon’s acceleration interfaces.
From Smartphones to PCs
Qualcomm’s Snapdragon chips have long been successful in smartphones, embedded in devices from Huawei, Xiaomi, and Samsung. Now, Qualcomm is pushing these SoCs into Windows notebooks, hoping to replicate that success. The Snapdragon X Elite and Snapdragon X Plus are two such chips designed for laptops. They differ in core count (12 vs. 10 Oryon high-performance cores) and GPU complexity, but both run Windows 10 or 11 in native ARM editions.
Because it’s a different architecture from x86, developers and users face unique hurdles: not all Windows apps are compiled for ARM, and many AI frameworks default to Nvidia’s CUDA or x86 instructions. Qualcomm and Microsoft have responded by releasing dedicated tools and libraries to simplify porting. Qualcomm’s AI-Hub and the Hugging Face community maintain curated repositories of model conversions for local use.
Copilot+ PCs and the “KI-Begleiter”
Some newer systems bearing “Copilot+ PC” branding include a “Copilot” key beside the right Alt, as well as Microsoft’s own built-in AI assistants for Windows on ARM. For instance, the “KI-Begleiter” in Windows 11 can chat and generate text based on prompts. However, early checks show the local NPU barely engages—most prompts appear to be resolved server-side, with only a tiny spike in the NPU’s activity. So while marketing slogans suggest hardware AI acceleration, Microsoft’s first-party apps often still rely on cloud inference.
Nonetheless, third-party software demonstrates the Snapdragon’s AI horsepower:
- GIMP (free image editor) includes optional AI plugins for Stable Diffusion 1.5 using Qualcomm’s AI Engine Direct (QNN). The NPU usage spikes noticeably, accelerating text-to-image generation within seconds.
- Stable Diffusion WebUI also benefits from Qualcomm’s AI runtime (QAIRT) for local inference. With a specific command-line setup, you can achieve moderate speed (15–30 s per 512×512 image), though advanced features like ControlNet or SDXL remain unsupported in the current QNN-based solutions.
Running Language Models Locally
Tools like Ollama and LM Studio can execute Llama-based GPT models on ARM64 hardware, albeit currently only on the CPU. On the Ryzen 7 8700G or Apple’s M4 Pro, they may achieve high token-per-second rates thanks to optimised matrix operations. On Snapdragon X notebooks, they also run with moderate speed—28–38 tokens/s in tests—though the NPU remains largely unused for large language models at present. Qualcomm and Microsoft have indicated future support for local NPU inference in these frameworks.
Developer Tools and Libraries
Behind the scenes, there’s a complex software stack to harness Snapdragon’s GPU/NPU acceleration:
- Qualcomm AI Engine Direct (QNN): The low-level interface to the SoC’s accelerators, used by devs seeking maximum control.
- ONNX Runtime: A cross-platform layer offering “execution providers” (EPs) for hardware acceleration, including DirectML, QNN, and others.
- DirectML: Microsoft’s abstraction for GPU/NPU usage within DirectX, letting PyTorch or ONNX ops run on various hardware.
- Olive: A higher-level pipeline that applies model optimisations and quantisations to fit the target device’s capabilities.
Depending on the complexity of your model, you can aim for the low-level QNN layer for highest speed or opt for ONNX/DirectML for cross-hardware compatibility. Meanwhile, the Windows Performance Recorder (WPR) and Analyzer can offer detailed profiling to identify bottlenecks, while dev-friendly frameworks like llama.cpp make it simpler to load quantised large language models on CPU cores.
Real Apps Using NPU
Besides open-source tools, some commercial apps already exploit the Snapdragon X NPU:
- Affinity Photo 2 (ARM64 edition) implements AI-based enhancements such as subject selection.
- Blender and Capture One accelerate tasks like colour correction or text-to-image generation with the NPU.
- Several DAWs, including future ARM64 releases of Reaper, Cubase, and Nuendo, plan to harness the Snapdragon’s AI blocks for real-time audio processing.
Conclusion
Snapdragon-based Windows notebooks can now run local AI models with minimal power draw, an appealing prospect for productivity tasks, image editing, or AI-based assistants. Yet the software ecosystem is still catching up. Developers must adapt their workflows to ARM64, and many frameworks remain heavily oriented to Nvidia’s CUDA or x86 code paths. Microsoft’s approach is more fragmented than Apple’s integrated Mac environment.
Still, for certain workloads, the combination of the CPU, Adreno GPU, and a potent NPU is compelling. As local generative AI proliferates, these Copilot+ PCs may showcase how ARM Windows devices can keep up with Apple’s M-series hardware—provided developers invest the effort to optimise for Qualcomm’s new platform. The payoff is an ultra-mobile system that can speed up advanced ML tasks without guzzling battery power.