Qualcomm has unveiled its AI Hub, an all-inclusive library of pre-optimized AI models ready for use on devices running on Snapdragon and Qualcomm platforms.
These models support a wide range of applications including natural language processing, computer vision, and anomaly detection, and are designed to deliver high performance with minimal power consumption, a critical factor for mobile and edge devices.
The AI Hub library currently includes more than 75 popular AI and generative AI models including Whisper, ControlNet, Stable Diffusion, and Baichuan 7B. All models are bundled in various runtimes and are optimized to leverage the Qualcomm AI Engine’s hardware acceleration across all cores (NPU, CPU, and GPU). According to Qualcomm, they’ll deliver four times faster inferencing times.
Documentation and tutorials provided
The AI Hub also handles model translation from the source framework to popular runtimes automatically. It works directly with the Qualcomm AI Engine direct SDK and applies hardware-aware optimizations. Developers can search for models based on their needs, download them, and integrate them into their applications, saving time and resources.
The AI Hub also provides tools and resources for developers to customize these models, and they can fine-tune them using the Qualcomm Neural Processing SDK and the AI Model Efficiency Toolkit, both available on the platform.
To use the AI Hub, developers need a trained model in PyTorch, TorchScript, ONNX, or TensorFlow Lite format, and a good understanding of the deployment target, which can be a specific device (like Samsung Galaxy S23 Ultra) or a range of devices.
The AI Hub is not exclusively for experienced developers however. It also serves as a learning platform, providing comprehensive documentation and tutorials for those venturing into the world of AI.
Qualcomm plans to regularly update the AI Hub with new models and support for additional platforms and operating systems. Developers can sign up to access these models on cloud-hosted devices based on Qualcomm platforms and get early access to new features and AI models.
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This post originally appeared on TechToday.