M5Stack LLM (large language model) Module Kit (AX630C)

Module LLM Kit is a smart modular kit focused on offline AI inference and data communication interface applications. It integrates the Module LLM and Module13.2 LLM Mate modules to meet the offline AI inference and data interaction requirements across various scenarios.

Module LLM is an integrated offline large language model (LLM) inference module designed specifically for terminal devices that require efficient and intelligent interaction. Whether for smart home applications, voice assistants, or industrial control, Module LLM delivers a smooth and natural AI experience without relying on the cloud, ensuring privacy, security, and stability.

Module13.2 LLM Mate Module provides a variety of interface functions to facilitate system integration and expansion. It achieves stacked power supply with Module LLM via the M5BUS interface; its built-in CH340N USB conversion chip offers USB-to-serial debugging functionality, while the Type-C interface is used for USB log output. Additionally, the RJ45 interface works with the onboard network transformer to extend to a 100 Mbps Ethernet port and core serial port (supporting SBC applications); the FPC-8P interface connects directly to Module LLM, ensuring stable serial communication; furthermore, an HT3.96*9P solder pad is reserved for DIY expansion.

The Module LLM module integrates the StackFlow framework along with the Arduino/UiFlow libraries, allowing edge intelligence to be implemented with just a few lines of code. Powered by the advanced AiXin AX630C SoC processor and featuring a high-efficiency NPU delivering 3.2 TOPS with native support for Transformer models, it effortlessly handles complex AI tasks. Equipped with 4GB LPDDR4 memory (1GB for user applications and 3GB dedicated to hardware acceleration) and 32GB eMMC storage, it supports parallel multi-model loading and chained inference, ensuring smooth multitasking. With an operating power consumption of only about 1.5W, it is far more energy efficient than similar products, making it ideal for long-term operation.

Module LLM is compatible with multiple models and comes pre-installed with the Qwen2.5-0.5B large language model, featuring built-in functions including KWS (wake word), ASR (speech recognition), LLM (large language model), and TTS (text-to-speech). It also supports apt-based rapid updates of software and model packages. By installing the openai-api plugin, it becomes compatible with the OpenAI standard API, supporting chat, conversation completion, speech-to-text, and text-to-speech among various application modes. The official apt repository offers abundant large model resources—including deepseek-r1-distill-qwen-1.5b, InternVL2_5-1B-MPO, Llama-3.2-1B, Qwen2.5-0.5B, and Qwen2.5-1.5B—as well as text-to-speech models (whisper-tiny, whisper-base, melotts) and visual models (such as yolo11 and other SOTA models). The repository is continuously updated to support the most cutting-edge model applications, meeting the demands of complex AI tasks.

Module LLM Kit is plug-and-play, and when paired with the M5 host, it provides an instant AI interactive experience. Users can seamlessly integrate it into existing smart devices without cumbersome setup, quickly enabling intelligent features and enhancing device performance. This product is ideal for offline voice assistants, text-to-speech conversion, smart home control, interactive robots, and more.

Features

• Offline inference, 3.2 TOPS at INT8 precision
• Integrated KWS (wake word), ASR (speech recognition), LLM (large language model), TTS (text-to-speech)
• Parallel multi-model processing
• Onboard 32GB eMMC storage and 4GB LPDDR4 memory
• Onboard microphone and speaker
• Serial communication
• SD card firmware upgrade
• Supports ADB debugging
• RGB status LED
• Built-in Ubuntu system
• Supports OTG functionality
  

Includes

• 1 x Module LLM
• 1 x Module LLM Mate
• 2 x FPC-8P Wire
  

Applications

• Offline voice assistants
• Text-to-speech conversion
• Smart home control
• Interactive robots

Specifications