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1.0.0

AIBOX-3588S

  • Preface

User Manual

  • Introduction
  • Interface definition
  • Login
  • Watchdog
  • RTC

Upgrade Firmware

  • Boot mode description
  • Upgrade the firmware via USB cable
  • MaskRom Mode
  • Upgrade the firmware via SD card

NPU Usage

  • Large Language Model
  • Artificial Intelligence Algorithm Deployment

Resource download

AIBOX-3588S Manual
AIBOX-3588S
Spec Buy

AIBOX-3588S Equipped with Rockchip octa-core 64-bit processor RK3588S, it adopts advanced process technology, high performance and low power consumption, built-in ARM Mali-G610 MP4 quad-core GPU, integrated 6 TOPS computing power NPU, supports private deployment of ultra-large-scale parameter models under Transformer architecture, supports traditional network architectures such as CNN, RNN, LSTM, supports multiple deep learning frameworks, supports custom operator development, and supports Docker container management technology. One box can meet personalized AI deployment.

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Welcome to AIBOX-3588S Manual¶

AIBOX-3588S

  • Preface
    • Overview
    • OS version
    • Document current version
    • Revision history

User Manual

  • Introduction
    • Computer Size
  • Interface definition
  • Login
    • Console Login
    • HDMI Login
  • Watchdog
  • RTC
    • Introduction
    • Interface Usage

Upgrade Firmware

  • Boot mode description
    • Preface
    • How to get the Firmwares
    • Upgrade method
    • Boot media
    • Boot mode
  • Upgrade the firmware via USB cable
    • Introduction
    • Preparatory Tools
    • Prepare Firmware
    • Install the Upgrade Tool
    • Enter Upgrade mode
    • Upgrade the firmware
    • FAQs
  • MaskRom Mode
    • Introduction
  • Upgrade the firmware via SD card
    • Introduction
    • Preparatory Tools
    • Operation Steps

NPU Usage

  • Large Language Model
    • 1.RKLLM Introduction
    • 2.RKLLM-Toolkit Installation
    • 3.Large Language Model Deployment Example
    • 4.Others
    • 5.FAQs
  • Artificial Intelligence Algorithm Deployment
    • 1.1 NPU Usage
    • 1.2 RKNN-Toolkit2
    • 1.3 RKNPU2 Usage
    • 1.4 RKNN-Toolkit Lite2 Introduction
    • 1.5 Detailed Development Documentation
    • 1.6 FAQs
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