Description The Module LLM Kit is a modular offline AI inference kit built around the Module LLM and Module13.2 LLM Mate. It brings large-language-model voice interaction, data communication, and host connectivity into a stackable M5Stack module design. Based on the AX630C SoC, the Module LLM Kit provides 3.2 TOPS INT8 NPU performance, 4GB LPDDR4 memory, and 32GB eMMC storage. It can run built-in KWS, ASR, LLM, and TTS functions locally without relying on a cloud service, helping privacy-sensitive and network-limited projects keep voice and text processing on the device. Notice: Before connecting the FPC cable, lift the connector latch first. After inserting the cable fully, press the latch down to secure it. Before connecting the Module LLM FPC cable, remove the device frame as recommended. The FPC connector is located under the SPK module, so remove the SPK module before operation. Model replacement requires AXERA-specific model processing. Existing general models from the market cannot be used directly. Features Module LLM Kit with Module LLM and Module13.2 LLM Mate for offline AI inference. AX630C dual-core Cortex-A53 SoC with 3.2 TOPS INT8 NPU acceleration and native Transformer support. 4GB LPDDR4 memory, including 1GB for user applications and 3GB for hardware acceleration. 32GB eMMC5.1 storage for the Ubuntu-based system, models, and application data. Built-in KWS, ASR, LLM, and TTS functions for local voice interaction. Parallel multi-model processing, onboard microphone, speaker, RGB status LEDs, and ADB debugging. Module13.2 LLM Mate adds M5-Bus power stacking, CH340N USB conversion, Type-C log output, Ethernet, and expansion pads. Development support for Arduino, UiFlow2, StackFlow, apt-based model updates, and OpenAI API-compatible workflows. Specifications Processor AX630C dual Cortex-A53 at 1.2GHz AI performance 3.2 TOPS INT8 NPU; up to 12.8 TOPS INT4 Memory 4GB LPDDR4, with 1GB for user applications and 3GB for hardware acceleration Storage 32GB eMMC5.1 System Ubuntu-based system with StackFlow framework support Built-in functions KWS, ASR, LLM, and TTS Microphone MSM421A Audio driver AW8737 Speaker 8 ohm at 1W, 2014 cavity speaker RGB LED 3 x RGB LED at 2020 package, driven by LP5562 Communication Serial communication, default 115200bps@8N1, adjustable USB conversion CH340N Network RJ45 100Mbps Ethernet with onboard network transformer Expansion FPC-8P connection and HT3.96 x 9P solder pad Upgrade methods SD card and Type-C Power consumption No-load 5V at 0.5W; full-load 5V at about 1.5W Operating temperature 0 to 40 degrees C Module dimensions 54.0 x 54.0 x 13.0mm Mate board dimensions 54.0 x 54.0 x 19.7mm Combined weight 36.7g Applications Offline voice assistants that need local speech recognition, language understanding, and speech output. Smart home controllers that continue operating when cloud access is unavailable. Interactive robots and embedded terminals using the Module LLM Kit for local AI dialogue. Privacy-focused AI gateways, education projects, and prototype devices for edge inference. Product List 1 x Module LLM 1 x Module LLM Mate 2 x FPC-8P Wire Other Content The Module LLM Kit communicates with host devices through serial interfaces and can expose logs, Ethernet, and expansion signals through the Mate board. Use the StackFlow resources and connection guide to match the wiring, FPC connection, model update method, and host workflow before deployment. Resources Module LLM Kit product document Module LLM schematic PDF Module13.2 LLM Mate schematic PDF AX630C processor databrief PDF Module LLM Arduino quick start guide Module LLM UiFlow2 quick start guide Module LLM ADB, UART, and SSH connection guide Module LLM software update guide Module LLM image update guide Module LLM Arduino library repository Module LLM Arduino API reference Module LLM UiFlow2 API reference FAQ Can this kit run AI features without cloud access? Yes. The Module LLM Kit is designed for local KWS, ASR, LLM, and TTS processing, so supported voice and language workflows can run on the device. Which model is preinstalled? The module is listed with Qwen2.5-0.5B preinstalled, and the apt repository provides additional supported model packages. Can any downloaded large language model be used directly? No. The supported models use an AXERA-specific format and must be processed before they can run correctly on the hardware.
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