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
Who is this for?
This Learning Path is for developers and engineers who want to run multimodal image, audio, and text models on Armv9 Linux systems using MNN as a portable, CPU-first inference runtime. It is aimed at readers who are comfortable building software from source and want a reproducible on-device workflow without quantization or heterogeneous scheduling.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Build MNN natively on an Armv9 Linux system for multimodal inference
- Verify a CPU-only Omni model workflow with text, vision, and audio prompts
- Create a reproducible multimodal application flow that combines image and audio inputs into an actionable restock ticket
Prerequisites
Before starting, you will need the following:
- An Armv9 Linux device with at least 32 GB of available disk space, for example a Radxa Orion O6
- Familiarity with the Linux command line, Git, and building C++ projects with CMake
- Internet access to download source code, model assets, and sample data
Summary
This summary was 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.
llm_demo to establish a text-only baseline. Then, you’ll add a vision shelf audit that identifies the sparsest priority zone, and handle unclear images with NOT_SURE. Finally, you’ll convert an audio prompt into a reproducible, semicolon-separated restock ticket.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.
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.NOT_SURE.