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DFR1146

Name: DFRobot DFR1146
Brand: DFRobot
SKU: 426-DFR1146
Price: 3302280 IDR
Availability: InStock

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Mouser No:

426-DFR1146

Mfr. No:

DFR1146

Mfr.:

DFRobot

Description:

Single Board Computers LattePanda Mu - A Micro x86 Compute Module (N100 CPU,8GB RAM,64GB eMMC)

Lifecycle:

New Product:

New from this manufacturer.

Datasheet:

DFR1146 Datasheet (PDF)

More Information

Learn more about DFRobot DFR1146

In Stock: 98

Stock:: 98 Can Dispatch Immediately

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Factory Lead Time:: 4 Weeks Estimated factory production time for quantities greater than shown.

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Minimum: 1 Multiples: 1

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Ext. Price:

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Pricing (IDR)

Qty.	Unit Price	Ext. Price
	Rp3.302.280	Rp3.302.280

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Product Attribute	Attribute Value	Select Attribute
Manufacturer:	DFRobot
Product Category:	Single Board Computers
RoHS:
Series:	Mu
Processor Brand:	Intel
Processor Type:	QuadCore
Core:	Intel N100
Frequency:	3.4 GHz
Operating Supply Voltage:	9 V to 20 V
Interface Type:	I2C, USB
Minimum Operating Temperature:	0 C
Maximum Operating Temperature:	+ 60 C
Dimensions:	69.6 mm x 60 mm
Packaging:	Bulk
Brand:	DFRobot
Memory Size:	8 GB
Memory Type:	LPDDR5
Number of Cores:	4
Product Type:	Single Board Computers
Factory Pack Quantity:	1
Subcategory:	Computing
Tradename:	LattePanda

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Filter Document:

Datasheet

DFR1146 Datasheet (PDF)

Product Overview

LattePanda Mu Micro x86 Compute Module_Product Overview (PDF)

CAHTS:: 8471500090
USHTS:: 8471500150
JPHTS:: 847150000
TARIC:: 8471500000
MXHTS:: 8471500100
ECCN:: EAR99

LattePanda Mu Micro x86 Compute Module

DFRobot LattePanda Mu Micro x86 Compute Module features an Intel N100 quad-core processor, 8 GB LPDDR5 memory, and 64 GB storage. This module provides a wide range of interfaces, including 3 HDMI/DisplayPort, 8 USB 2.0, up to 4 USB 3.2, and up to 9 PCIe 3.0 lanes. The LattePanda Mu compute module is based on the Intel N100 quad-core processor with a 3.4GHz turbo frequency, offering ample performance and multitasking capabilities for various applications. This module is compact in size, measuring 69.6mm x 60mm, thus enabling integration into devices with limited space.