
LattePanda Mu - A Micro x86 Compute Module (N100 CPU, 16GB RAM, 64GB eMMC)
The LattePanda Mu is a credit card-sized x86 compute module powered by an Intel N100 quad-core processor with 16GB LPDDR5 memory and 64GB eMMC storage. Despite measuring just 69.6 × 60mm, it delivers desktop-class performance — scoring over double the Raspberry Pi 5 in CPU benchmarks.
The module exposes an extensive array of expansion pins including 3 display outputs, up to 4 USB 3.2 ports, 9 PCIe 3.0 lanes, and 64 GPIOs. Open-source carrier board design files (KiCad) make it straightforward to design custom carrier boards for your specific application. DFRobot also offers ready-made lite and full-function carrier boards for rapid development.
Key Features
- Intel N100 Processor – 4 cores, 4 threads, up to 3.4GHz turbo frequency
- 16GB LPDDR5 Memory – Full-speed 4800MT/s with IBECC support
- 64GB eMMC 5.1 Storage – On-board storage, ready to boot
- Configurable TDP – Adjustable from 6W (passive cooling) to 35W (maximum performance)
- Triple Display Output – 3× HDMI/DisplayPort, up to 4096 × 2160 @ 60Hz
- Rich I/O Expansion – 9 PCIe 3.0 lanes, 2 SATA 3.0, up to 4 USB 3.2 (10Gbps), 8 USB 2.0, I2C, UART, and 64 GPIOs
- Multi-OS Support – Windows 10, Windows 11, and Ubuntu
- Open-Source Carrier Design – KiCad files and libraries available for custom carrier board development
Specifications
- Processor – Intel N100, 4 cores, up to 3.4GHz
- Memory – 16GB LPDDR5 4800MT/s (IBECC supported)
- Storage – 64GB eMMC 5.1
- Display – 3 outputs, max 4096 × 2160 @ 60Hz
- Power Input – 9–20V DC
- TDP Range – 6W–35W (configurable)
- Operating Temperature – 0–60°C
- Humidity – 0–80% relative
- Dimensions – 69.6 × 60mm
Ideal For
- Custom embedded x86 systems and industrial applications
- Edge computing and IoT gateways
- Digital signage and kiosk systems
- Compact Windows or Linux workstations
- Prototyping custom single-board computer designs
Package Contents
- 1× LattePanda Mu Compute Module (N100, 16GB RAM, 64GB eMMC)
- 1× Product Manual
Resources
Product Information
Product Information
Shipping & Returns
Shipping & Returns
Description
The LattePanda Mu is a credit card-sized x86 compute module powered by an Intel N100 quad-core processor with 16GB LPDDR5 memory and 64GB eMMC storage. Despite measuring just 69.6 × 60mm, it delivers desktop-class performance — scoring over double the Raspberry Pi 5 in CPU benchmarks.
The module exposes an extensive array of expansion pins including 3 display outputs, up to 4 USB 3.2 ports, 9 PCIe 3.0 lanes, and 64 GPIOs. Open-source carrier board design files (KiCad) make it straightforward to design custom carrier boards for your specific application. DFRobot also offers ready-made lite and full-function carrier boards for rapid development.
Key Features
- Intel N100 Processor – 4 cores, 4 threads, up to 3.4GHz turbo frequency
- 16GB LPDDR5 Memory – Full-speed 4800MT/s with IBECC support
- 64GB eMMC 5.1 Storage – On-board storage, ready to boot
- Configurable TDP – Adjustable from 6W (passive cooling) to 35W (maximum performance)
- Triple Display Output – 3× HDMI/DisplayPort, up to 4096 × 2160 @ 60Hz
- Rich I/O Expansion – 9 PCIe 3.0 lanes, 2 SATA 3.0, up to 4 USB 3.2 (10Gbps), 8 USB 2.0, I2C, UART, and 64 GPIOs
- Multi-OS Support – Windows 10, Windows 11, and Ubuntu
- Open-Source Carrier Design – KiCad files and libraries available for custom carrier board development
Specifications
- Processor – Intel N100, 4 cores, up to 3.4GHz
- Memory – 16GB LPDDR5 4800MT/s (IBECC supported)
- Storage – 64GB eMMC 5.1
- Display – 3 outputs, max 4096 × 2160 @ 60Hz
- Power Input – 9–20V DC
- TDP Range – 6W–35W (configurable)
- Operating Temperature – 0–60°C
- Humidity – 0–80% relative
- Dimensions – 69.6 × 60mm
Ideal For
- Custom embedded x86 systems and industrial applications
- Edge computing and IoT gateways
- Digital signage and kiosk systems
- Compact Windows or Linux workstations
- Prototyping custom single-board computer designs
Package Contents
- 1× LattePanda Mu Compute Module (N100, 16GB RAM, 64GB eMMC)
- 1× Product Manual























