Build a multimodal retail restocking assistant on Armv9 with MNN
Introduction
Run multimodal inference with MNN on Armv9
Build MNN and prepare an Omni model on Armv9
Validate text-only inference with an Omni model on Armv9
Run a vision retail shelf audit with MNN Omni
Convert spoken restock notes into structured tickets with MNN Omni
Build a single-shot multimodal restock ticket with MNN Omni
Next Steps
Build a multimodal retail restocking assistant on Armv9 with MNN
Introduction
Run multimodal inference with MNN on Armv9
Build MNN and prepare an Omni model on Armv9
Validate text-only inference with an Omni model on Armv9
Run a vision retail shelf audit with MNN Omni
Convert spoken restock notes into structured tickets with MNN Omni
Build a single-shot multimodal restock ticket with MNN Omni
Next Steps
Review frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
Use the
llm_demo binary to load the prebuilt Omni MNN model package. If the package loads without errors and a simple prompt produces tokens, continue to the next steps.The baseline produces a predictable response with visible token generation. Record the output so you can compare behavior after adding vision and audio inputs.
The audit estimates coverage for the top, middle, and bottom shelf levels. It identifies the sparsest priority zone and gives a short reason. If the image is unclear, it returns
NOT_SURE.No. The workflow uses a native CPU-only MNN build, and the vision reasoning runs locally without cloud round trips.
The result is a single-line, semicolon-separated ticket for predictable parsing and consistent terminal display. Combine it with the vision audit’s priority zone to create an actionable restock ticket.