Odroid M1 8GB

Odroid M1 and microSD card prices have risen significantly over the last couple of months and prices are expected to continue increasing due to rising chip prices. In addition, supplies are currently constrained as well. We will do our best to fill your order as quickly as possible.

The CPU has four ARM Cortex-A55 processors with low power consumption and high efficiency operation at 2Ghz. The CPU and GPU performance is quite similar to our ODROID-C4.

A larger 8GB of LPDDR4 DRAM Memory is available while there is also a 4GB model for reduced cost.

Petitboot pre-installed into the on-board 16MiB of SPI-Flash Memory helps manage various OS and kernel versions easily and supports booting from microSD, eMMC, NVMe, SATA, and USB storage devices. In addition, since on-line OS installation using a network is possible, the cumbersome procedure of downloading and flashing an OS image is dramatically reduced.

A standard size 22mm x 80mm M.2 NVMe storage device can be directly installed on the ODROID-M1 board. The M.2 connector has PCIe 3.0 2-lane configuration which can deliver a blazing fast 1600MB/sec of sequential access performance. Note that M.2 SATA storage devices can not be used. The M.2 slot supports only a PCIe interface (M-Key).

PicoCluster takes Odroid M1 to the next level by creating clusters or groups of them to demonstrate and teach beginning to very advanced computing principles. We sell clusters of 3, 5, 10 and 20 computers (called nodes), or you can build your own with either our Starter or Advanced kits!

What Is a PicoCluster? Why You Might Need Your Own Mini Data Center

Why Choose PicoCluster?

At PicoCluster, we blend state-of-the-art technology with user-friendly design. Our clusters are pre-assembled and tested, ensuring you receive a reliable and fully functional product. The PicoCluster Raspberry Pi 5 offers a powerful, versatile, and compact solution for all your computing needs, making it the perfect choice for developers, educators, and hobbyists alike.

Applications:

• Parallel Computing and Distributed Systems
• Development and Testing of Cloud Applications
• Learning and Experimenting with Kubernetes
• IoT Development and Prototyping
• Personal Cloud and Home Lab