What is the M5Stack LLM Module and How to Use it?
12 Feb 2025
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What is the M5Stack LLM Module?
The M5Stack LLM Module is an integrated offline Large Language Model (LLM) inference module designed for endpoints that require efficient and intelligent interaction. Whether for smart homes, voice assistants or industrial control, Module LLM provides a smooth and natural AI experience without relying on the cloud, ensuring privacy and stability. Integrated with the StackFlow framework and Arduino/UiFlow libraries, intelligent functions can be easily implemented with just a few lines of code.
Powered by the advanced AX630C SoC processor, it integrates a 3.2 TOPs high-efficiency NPU with native support for Transformer models to handle complex AI tasks with ease. Equipped with 4GB of LPDDR4 memory (1GB available for user applications, 3GB dedicated for hardware acceleration) and 32GB of eMMC storage, it supports parallel loading and sequential inference of multiple models for smooth multitasking. The main chip is manufactured using TSMC's 12nm process, with an operating power consumption of approximately 1.5W, making it highly efficient and suitable for long-term operation.
It has an integrated microphone, speaker, TF memory card, USB OTG, and RGB status light to support voice interaction and data transfer for a wide range of applications. The module offers flexible expansion: the on-board SD card slot supports cold/hot firmware upgrades, and the UART communication interface simplifies connection and debugging, ensuring continuous optimisation and expansion of the module's functionality. The USB port supports master-slave auto-switching, acting as a debugging port as well as allowing connection to additional USB devices such as cameras. Users can purchase the LLM debugging kit to add a 100Mbps Ethernet port and a kernel serial port to use it as an SBC.
Overview
Hardware:
Microcontroller: Typically features an AX630C@Dual Cortex A53 1.2 GHz MCU, which is MAX.12.8 TOPS @INT4 and 3.2 TOPS @INT8.
Memory: 4GB LPDDR4 (1GB system memory + 3GB dedicated for hardware acceleration).
Storage: 32GB eMMC5.1
Upgrade Port: SD card / Type-C port
Features
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Offline inference, 3.2T@INT8 precision computing power
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Integrated KWS (wake word), ASR (speech recognition), LLM (large language model), TTS (text-to-speech generation)
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Multi-model parallel processing
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Onboard 32GB eMMC storage and 4GB LPDDR4 memory
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Onboard microphone and speaker
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Serial communication
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SD card firmware upgrade
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Supports ADB debugging
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RGB indicator light
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Built-in Ubuntu system
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Supports OTG functionality
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Compatible with Arduino/UIFlow
Usage and Development
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Hardware Setup:
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Connect the LLM Module to your M5Stack core or other compatible device via the Grove port.
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Ensure you have an antenna connected for optimal range.
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Software Development:
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Install the LoRaWAN library for ESP32 in the Arduino IDE or use the appropriate firmware for MicroPython if supported.
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Configure the module with your LoRaWAN network settings (e.g., DevEUI, AppEUI, AppKey).
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Write code to handle LoRaWAN communication, including joining the network, sending data, and receiving commands.
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Integration with IoT Platforms:
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Many LoRaWAN networks integrate with IoT platforms like The Things Network (TTN) or commercial network operators. Set up your device to communicate with these platforms for data management and analytics.
Conclusion
The M5Stack LLM Module is multi-model compatible and comes pre-installed with the Qwen2.5-0.5B speech model. It provides KWS (wake word), ASR (automatic speech recognition), LLM (large language model), and TTS (text-to-speech) functionality, with support for standalone calls or automatic pipeline transfer for ease of development. Future support includes Qwen2.5-1.5B, Llama3.2-1B, and InternVL2-1B models, enabling hot model updates to keep pace with community trends and address various complex AI tasks. Vision capabilities include support for CLIP, YoloWorld, and future updates for DepthAnything, SegmentAnything, and other advanced models to enhance intelligent detection and analysis.
Plug and play with M5 hosts, the LLM module provides an easy-to-use AI interaction experience. Users can quickly integrate it into existing smart devices without complex settings, enabling intelligent functionality and enhancing device intelligence. This product is suitable for offline voice assistants, text-to-speech conversion, smart home control, interactive robots and more.
How to Use the M5Stack LLM Module?
Today I'll show you how to use the M5Stack UIflow test the M5Stack LLM (large language model) Module.
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