Réf. fabricant K144
Vendu par m5stack à 69.24 €. Livré en France, port, TVA et droits de douane éventuels compris, il revient à environ 101.09 €. Prix relevés le 2026-09-16.
Le coût livré est une estimation pour un article seul expédié en France métropolitaine ; seul le panier du vendeur fait foi.
| Article | 69.24 € |
|---|---|
| Frais de port | 15.00 € |
| TVA (20 %) | 16.85 € |
| Total livré | 101.09 € |
Estimation pour un article seul livré en France métropolitaine, expédié hors UE : des droits de douane et frais de dédouanement peuvent s'ajouter, facturés par le transporteur.
| Poids | 178 g |
Description 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 M5-Bus 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 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 simila
