The Cerebro Clusterboard is an upcoming modular “motherboard” designed to host up to four interchangeable compute modules – such as Raspberry Pi CM4/CM5, NVIDIA Jetson NX, or Radxa CM5 – on a single micro-ATX/1U rack form factor , complete with an upgradable board management controller (BMC), 13 M.2 expansion slots, and multiplexed HDMI/USB channels. You’ll discover what a clusterboard is, how it operates under the hood, what sets Cerebro apart from existing solutions, which single-board computers (SBCs) it supports (and why mixed-module flexibility is such a breakthrough), practical tips for prospective users, illustrative use cases from homelabs to edge AI, and a concise wrap-up to tie it all together.
What Is a Clusterboard?
A clusterboard is essentially a specialized baseboard that lets you plug in multiple compact “compute modules” – often standardized as COM (Computer-On-Module) or SBC (Single-Board Computer) formats – so they can operate together as a cluster for parallel processing tasks. Rather than cobbling together standalone boards with zip ties and DIY cables, a clusterboard provides a unified backplane, power distribution, network switching, and I/O multiplexing, creating a neat, rack-mountable solution for homelabs, AI research, edge computing, and more.
In practice, each compute node attaches via high-density connectors; the board handles inter-node networking, shared storage or accelerator slots, and centralized management so you can deploy container clusters, train small neural networks, compile code in parallel, or run distributed services without dealing with messy wiring.
How Clusterboards Work
At the heart of any clusterboard is the backplane: a copper-traced PCB that routes power, data, and control signals between nodes and shared resources. In Cerebro’s case, each of the four module slots connects to:
- A gigabit-VLAN managed switch with two external RJ45 ports for clustered networking
- A USB 3.1 Gen2 & USB 2.0 multiplexer, allowing you to route host-side peripherals to any node as needed
- An HDMI multiplexer to direct display output from any module to a single monitor
Beyond networking and I/O, Cerebro integrates 13 M.2 slots (up to three per node) for SSDs, Wi-Fi/5G cards, or AI accelerator modules, plus a real-time clock, status LEDs, lockable I/O headers (GPIO, SPI, I²C, serial), a nano-SIM slot, microSD slot, and up to 72 V power input. All of this sits on a micro-ATX board that fits into standard 1U rack cases, keeping your setup compact and professional.
Image Credit: cerebro-board.com/
What Makes the Cerebro Clusterboard Different
While several clusterboards exist, Cerebro stands out thanks to its truly modular approach: it isn’t locked to one vendor’s compute module. Instead, it embraces any COM built to the Raspberry Pi Compute Module standard, plus key NVIDIA Jetson and Radxa variants, enabling you to mix and match based on performance, power, or budget needs.
On top of that, Cerebro features an upgradable BMC for out-of-band management (power cycling, health monitoring, remote console), 13 M.2 expansion slots, and multiplexed video/USB channels – all in a micro-ATX/1U rack form – so whether you’re scaling from a small homelab to a production edge-AI deployment, your hardware can grow without starting over.
Supported Single-Board Computers and Why It Matters
Cerebro officially supports:
- Raspberry Pi Compute Module 4 & 5 (CM4/CM5)
- Radxa CM5 / CM5 Lite
- NVIDIA Jetson modules (Nano, TX2 NX, Xavier NX, Orin NX, Orin Nano)
This breadth is a major feat because most cluster solutions bind you to one ecosystem (e.g., only CM4), limiting your upgrade path or locking you into a single price-performance point. With Cerebro, you could run four energy-efficient CM4s today, then swap in four Jetson Orins tomorrow for heavy AI inference – without changing the baseboard.
Being able to mix and match modules also optimizes costs: deploy two high-power nodes for critical workloads alongside two low-power modules for monitoring or lightweight tasks, all on the same board.
Tips for Prospective Users
- Budget Early – Early backers can reserve their Cerebro board for €429 (~$488) when the Kickstarter launches April 30, 2025, but retail pricing and shipping dates are still TBD.
- Plan Your Power & Cooling – With support for up to 72 V input, and multiple fan headers, ensure your case or rack has adequate cooling. High-power modules like Orin can draw significant amps.
- Chassis Compatibility – Cerebro fits micro-ATX cases and 1U racks. Verify depth/mounting holes on your enclosure before purchasing.
- Leverage the BMC – If you need remote management (power cycle, health stats, serial console), budget time to configure the upgradable BMC firmware for your network.
- Choose Modules Wisely – Match module capabilities to your workload: CM4/CM5 for lightweight server tasks; Jetson series for AI inference; Radxa CM5 if you need extra I/O.
- Engage the Community – Join Sparklab’s forums or Discord early to share adapter designs (if you have a non-standard COM) or to learn best practices from other backers.
Use Cases
- Edge AI Inference – Run real-time image classification or sensor data processing locally on Jetson modules, minimizing cloud latency.
- Homelab Clusters – Host containerized services (Kubernetes, Docker Swarm), CI/CD builders, or media servers across multiple Pi CM4 nodes.
- Parallel Compiling & Rendering – Distribute builds or 3D renders across four modules to shave hours off compile times.
- IoT & Network Gateways – Use one module as a firewall/router, another as an MQTT broker, and others for specialized processing (e.g., CAN-FD in automotive test rigs).
- Research & Education – Teach distributed computing concepts or AI clustering at universities without expensive server racks.
Conclusion
The Cerebro Clusterboard represents a significant leap in modular cluster computing: it unifies up to four diverse compute modules on a single, rack-ready micro-ATX board, complete with an upgradable BMC, extensive expansion via 13 M.2 slots, multiplexed HDMI/USB, and managed networking. By supporting Raspberry Pi CM4/CM5, NVIDIA Jetson NX series, and Radxa CM5 – and letting you mix and match – Cerebro offers unmatched flexibility for homelabs, edge AI deployments, parallel processing tasks, and beyond. If you need a scalable, tidy, and professional-grade cluster solution that adapts as your ambitions grow, keep an eye on Sparklab Solutions’ Kickstarter.
Visit the official Cerebro Clusrboard Website