qualcomm/ai-hub-models

Health v2 · maintenance only

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1195 stars · 208 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Qualcomm AI Hub Models is a curated collection of 300+ state-of-the-art machine learning models (vision, audio, speech, generative AI) pre-optimized for deployment on Qualcomm Snapdragon devices. It ships as a Python package and CLI for browsing, exporting, compiling, and profiling models to runtimes like TensorFlow Lite, ONNX, and Qualcomm AI Runtime (QNN).

Use cases

• deploy a machine learning model on a Snapdragon phone
• convert pytorch model to tflite for mobile
• benchmark model latency on qualcomm NPU
• download pre-optimized yolov7 for on-device inference
• run image classification on android device
• profile model performance on hexagon NPU
• find models optimized for qualcomm chipsets

When to choose

• You are deploying ML models to Qualcomm Snapdragon / Dragonwing hardware and want pre-validated, hardware-optimized assets
• You need on-device performance metrics (latency, memory, compute unit utilization) for Qualcomm chipsets
• You want a CLI/Python API to export models to TFLite, ONNX, or QNN runtimes

When to avoid

• You target non-Qualcomm hardware such as Apple Silicon, NVIDIA GPUs, or generic x86 servers
• You need to train or fine-tune models rather than deploy them
• You require a fully offline workflow - many features (compilation, profiling) require Qualcomm AI Hub Workbench cloud access

Facets

library · maturity active

machine-learning llm-inference image-processing speech-recognition computer-vision cli sdk machine-learning deep-learning artificial-intelligence mobile-development computer-vision embedded-systems developer-tools python windows cli embedded qualcomm on-device-ai model-optimization tflite onnx qnn snapdragon npu model-deployment pytorch android linux mobile

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markdown · JSON · MCP: product_card(name="qualcomm/ai-hub-models")