3 new models of RPI5 clusters are available including one with m.2 SSDs! Order now for quick delivery. Free shipping included in US for all orders.

PicoCluster and Desktop Data Centers

PicoCluster is the ultimate in Desktop Data Centers.  Every PicoCluster we make is designed to sit on a desk.  But their small size can be deceiving.  A PicoCluster is an actual data center, but contained in a cube instead of a room or a building.  It is a self-contained cluster of computers, usually rack mounted, that can be placed and used at locations outside of the corporate data center. They can distribute end-user applications closer to the actual users. A PicoCluster can be used to collect and process data at the source. They are often used at remote office locations or where data privacy and accountability are paramount. Desktop Data Centers are for places or applications where going to the cloud is not an option. They can also be used as a private cloud.

Why Use a Desktop Data Center

When most of us think of a data center, we think of a room or building with racks full of computers connected together.  Why would anyone want a micro version of one of those on their desk?  Here are a few reasons. 

  • Server time in a data center isn’t always available when you need it.
  • Having a desktop data center allows you to test applications before applying them in a large data center environment.
  • Being able to develop and deploy on a small scale can speed up implementation times.
  • Real-Time Results. For time-sensitive or small-scale data, being able to get the results in real-time can make a huge difference.
  • A desktop data center does not need to be connected to a network. 
  • Both purchase cost and ongoing energy costs are lower.
  • Ease of Deployment. A PicoCluster comes completely configured with Docker Swarm or Kubernetes. Just deploy your applications! Other Big Data software is also available.


Why Use a PicoCluster Instead of a Computer

While using your own computer to replicate a data center sounds simple and appears to be more cost effective, in reality it is neither.  While it is possible to run Big Data software on a desktop or laptop computer, in reality processor cores don’t communicate with each other very efficiently.  So it is difficult to truly replicate a clustered environment.  You may also find that your computer is not optimized for Big Data: you may need more RAM, an upgrade to an SSD instead of a SATA hard drive, and a NVIDIA GPU.  You will also need to make sure your quad core processor is at least an i7.  Then there is your operating system.  On a PC you’ll need to do a dual boot with both Linux and Windows for best results.  A virtual Linux machine on either a MAC or a PC will limit the amount of memory and processing performance you can expect.  If you are planning on using your computer for things other than Big Data, especially at the same time, you’ll find your performance is severely limited.

Cost effective?  Maybe in the short term, but not in the long run.  The cost to upgrade your current computer may be more than the cost of a dedicated data center.  And if you need to purchase a new computer, a PicoCluster will be cheaper from the start.  If you include the cost power consumption or the cost of your time in your calculations, using a PicoCluster will always save you money. 

Using an X86 server is another option.  These are larger physical machines that use virtual machines to divide the server into smaller computers to increase overall utilization.  While this is a viable option for some, they are not very cost effective and do not fit easily on a desktop. 

PicoCluster is the only company designing commercial desktop data centers from single board computers (SBC’s).  We build from smaller building blocks, making for more capable clusters. There is no need to run virtual machines. Instead, we run orchestration software like Docker Swarm or Kubernetes. These systems make the entire cluster available as a set of resources. The application simply states what resources it needs and the orchestrator takes care of everything else. This is the next step beyond virtual machines.

Can a Desktop Data Center Run “Real” Big Data Software

A PicoCluster is designed to run Big Data software.  Our cubes are available preconfigured with Image Sets such as Hadoop, ElasticSearch, Cassanda, Hypriot, Kubernetes or chose the stock PicoCluster Cluster Image.

PicoCluster is the only product in the industry that you can purchase preconfigured and ready to go. All you have to do is plug it in and start using Hadoop, Hypriot, or any of the other big data software.

You can use these clusters to run almost any kind of distributed or parallel software. Run your own LAMP cluster, Docker, Kubernetes, Hadoop, ElasticSearch, Cassandra and many others. Also learn languages like Javascript, Java, Python, R, and so on. Use for Development, QA, DevOps, Education, and more.


PicoCenter 48 is shipped with 48 computers, 192 processor cores, 48GB or 96GB ram, 1.5TB, 3TB, or 6TB of storage, 8 gigabit switches, and 2 power supplies all in a 17" x 17" x 7" enclosure. The PicoCenter 48 defines the next generation of desktop sized micro data centers. The total power draw is about 100 watts which makes it extremely energy efficient.  A full PicoCenter Rack can have 720 boards, 2880 processor cores, 1.5TB ram, 90TB of storage and consume only 2KW of power. That's a savings of up to 14KW versus a standard X86 rack configuration, saving money not only at the time of purchase, but also through your monthly power bills.

What are you waiting for? 


  • Hi There,

    My names Amar and I work for an IT-RESELLER. My customer is looking to purchase some of your products. Are we able to schedule and phone call on this number: 02038156680 to discuss this opportunity in further detail .

    Kind regards.

  • Hi There,

    My names Amar and I work for an IT-RESELLER. My customer is looking to purchase some of your products. Are we able to schedule and phone call on this number: 02038156680 to discuss this opportunity in further detail .

    Kind regards.


Leave a comment

Please note, comments must be approved before they are published