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ServeTheHome’s December 1, 2021 “Building the Ultimate x86 and Arm Cluster-in-a-Box” was a high-end integration showcase: one workstation-class x86 host paired with seven Arm-based NVIDIA BlueField-2 DPUs in a single chassis. It was not a current parts list or a like-for-like benchmark against compact Raspberry Pi clusters. Its enduring lesson is how workload, architecture, networking, power, cooling, storage, and physical fit shape a cluster design.
What was in the 2021 x86-and-Arm showcase?
Patrick Kennedy described the goal as: “Our goal was simple: build our vision of our cluster-in-a-box.” The system combined a large x86 workstation platform with seven Arm DPUs, rather than assembling a conventional stack of independent single-board computers. The figures below are specifications reported by ServeTheHome for its December 2021 build, not independently verified performance results.
| Part of the system | Reported configuration |
|---|---|
| Host processor | One AMD Ryzen Threadripper Pro 3995WX, 64 cores and 128 threads |
| Host memory | Eight 64 GB Micron DDR4-3200 ECC DIMMs, for 512 GB |
| Host motherboard | ASUS Pro WS WRX80E-SAGE SE WiFi |
| Arm nodes | Seven NVIDIA BlueField-2 DPUs, each with eight Arm Cortex-A72 cores at 2.0 GHz, 16 GB RAM, and 64 GB onboard flash |
| Storage | Approximately 8.2 TB in the source’s tally: two 3.84 TB Micron 7400 M.2 SSDs plus DPU flash |
| Networking | Approximately 1.4 Tbps in the source’s tally, including two 10Gbase-T ports and fourteen 100G ports from the seven DPUs; the author also reported 24 physical network connections on the rear, including management interfaces and Wi-Fi 6 |
| Chassis and cooling | Fractal Design Define 7 XL chassis and ASUS ROG Ryujin 360 RGB AIO CPU cooler |
| Aggregate counts | 120 cores / 184 threads and 624 GB RAM, as reported by ServeTheHome for the combined host and DPU resources |
Those components belong to an article-era showcase, not a present-day recommended build. The unusually high network capacity and consolidated physical layout distinguish it from lower-cost teaching or home-lab clusters.
How did the DPU and storage choices affect the design?
The build evolved from an initial plan for six DPUs with additional Samsung 980 Pro SSD storage on a Hyper M.2 x16 Gen4 card. Using onboard M.2 slots instead made room for a seventh DPU; when the system was configured around the additional storage card, it was used in place of that seventh DPU. This illustrates a practical constraint: PCIe slots, onboard storage, and accelerator or network cards compete for finite space and connectivity.
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- High Performance: Amlogic A311D - x4 2.2Ghz Cortex A73, x2 1.8Ghz Cortex A53 12nm SoC fabrication process for low heat 2T2R AC Wi-Fi with RSDB Features Bluetooth 5.0 USB 3.0 Available Gigabit Ethernet with WOL support LPDDR4/X USB-C PD for heavy applications
- Neural Network Accelerator: NPU: Supports a maximum frequency of 800MHz at 5.0 TOPS INT8 inference up to 1536 MAC Internal L2 cache (512KB) and system workspace buffer (1MB) Supports all major deep learning frameworks including TensorFlow and Caffe
- Maker Friendly: Stackable Design Programmable MCU 3 Programmable LEDs (Blue, Red and White) XPWR for external Power button Onboard SPI Flash Khadas TST Khadas KBI
- Business Applications Dual independent displays with GSensor H.264 / H.265 Encoding Supports multi-video decoding up to 4Kx2K@60fps+1x1080P@60fps VIN Power Input
- Rich IO: 40 Pin GPIO Header (USB, I2C, I2S, UART, ADC etc) 8-ch I2S for Microphone Array application (over M.2 Connector) MIPI-DSI MIPI-CSI Designed with GPIO Extender Chip
ServeTheHome described two ways to configure the BlueField cards. Arm processing could sit in the network path as a bump-in-the-wire, or the host and Arm CPU could access the ports simultaneously. Kennedy said the build used the latter. He also noted that putting the eight Arm cores in the data path usually reduces network performance; that is the article’s account of its configuration trade-off, not a universal benchmark claim.
What are the practical alternatives for a compact cluster?
Eight-node Raspberry Pi PoE example
Raspberry Pi’s official cluster tutorial documents an eight-node Raspberry Pi 4 example. Its parts list includes eight Pi 4 boards, eight PoE+ HATs, an 8-port Gigabit PoE-enabled switch, USB 3-to-Gigabit Ethernet and USB 3-to-SATA adapters, a SATA SSD, Ethernet cables, SD storage, and a case. These are example parts, not mandatory components for every Pi cluster.
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- Package Included: RasTech 4GB kit for Raspberry Pi 4 includes 1 x RPi 4 4GB board, 1 x 32GB card, 4 x pure copper heat sinks,1 x 5V 3A power supply with on/off, 2 x HD Out cables,1 x cooling fan, 2 x card reader, 1 x screwdriver. 12-month free warranty, lifetime service, 24-hour prompt and friendly response.Tips: The Card is in the package of the Pi 4 4GB board. You can insert the fan on the case directly. These 4 screws are given to your other cases.
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- Latest Upgrade: RasTech Raspberry Pi 4 model B has been completely upgraded. 1.5GHz quad core 64 bit ARM Cortex-A72 CPU. Dual monitor support with resolutions up to 4K. Transfers via 2 USB 3.0 & 2 USB 2.0 ports is faster up to 10 times. Dual band 802.11ac WiFi (2.4/5.0 GHz) and Bluetooth 5.0 module. Faster Gigabit Ethernet & Power-over-Ethernet support. Compatible with earlier Pi models.
- Safe to Use: The RasTech Pi 4 b 4gb features USB-C power supply with ON/OFF switch, was designed for simple and safe processing. Heatsinks and cooling fan will reduce chip hot-spots and increase thermal dissipation surface area, keep your Pi at peak performance. Protective Case will full protect your Pi board while doing your own projects.
- Computing for everybody: From education, home, industries large and small, people use the Pi 4b to learn programming skills, build hardware projects, do home automation, implement Kubernetes clusters and Edge computing, and even use them in industrial applications. Pi 4 model b has computing solutions to fit a wide range of applications. Let’s start building your cool new projects for Pi 4 kit right now!
With a compatible PoE+ HAT on each node and a switch that supports PoE with enough total power budget, one Ethernet cable can carry both network traffic and power to each node. For smaller setups, the tutorial also presents a USB hub or separate power supplies as alternatives. Decide the workload and storage needs before choosing board memory and node-local disks; the storage interface and capacity should follow the actual use case.
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A documented Kubernetes lab combines Raspberry Pi nodes with older Intel i5 mini PCs. Its author notes that the mini PCs offer more memory expansion than the Pi nodes but consume more power, and describes using a 16-port Gigabit Ethernet switch. For storage, the project discusses node-local SSDs and a centralized SAN. These are architectural options, not a current price guide; the project’s euro estimates are historical context rather than today’s market prices.
Rank #3
- No power adapter included
Single-system DPU showcase
The ServeTheHome design places a workstation host and seven DPUs in one large chassis, prioritizing substantial host resources and high aggregate network capacity. It is fundamentally different in scale and cost profile from a small PoE-powered Pi cluster or a mixed mini-PC lab. Jeff Geerling’s Turing Pi 2 approach was the lower-cost comparison point in the original article, but the cited material does not establish current platform availability or a present-day cost comparison.
Will Kubernetes work across x86 and Arm nodes?
The Kubernetes kubeadm guide, written for v1.37 when accessed, says kubeadm can be used on Raspberry Pi as well as laptops and cloud servers. It lists Linux hosts, at least 2 GiB RAM per machine, at least 2 CPUs on the control-plane node, and full network connectivity between cluster machines as baseline requirements. Kubernetes packages and binaries are built for amd64, 32-bit arm, arm64, ppc64le, and s390x; multi-platform control-plane and add-on images have been supported since v1.12.
Rank #4
- DeskPi Super6C is the Pi cluster board a standard size mini-ITX board to be put in a case with up to 6 RPI CM4 Compute Modules. For Case Kit, please refer to ASIN B0BGWZH6MP
- 6 RPI CM4 supported 1 Gbps RJ45 x2
- Onboard ON/OFF and Reset button
- 12V FAN Header x3
- DC 19v~24V or ATX 12V
That support does not guarantee that every application image or networking add-on runs on every architecture. Before deploying a mixed cluster, check that each workload image has a compatible image for the target CPU architecture and verify the chosen network provider’s platform support. Kubernetes also requires that the pod network not overlap host networks and that only one pod network be installed per cluster. Recheck the documentation for the Kubernetes version you plan to deploy, because version-specific requirements can change.
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How should you choose a cluster-in-a-box design?
Start with the work the cluster must do, then compare candidate designs against the same practical constraints. A compact cluster is not automatically more efficient, cheaper, or easier to manage; the cited projects describe different trade-offs and do not establish one universal winner.
Quick Recap
Best Value
- Featured here is a single case that can be configured for different applications. It is compatible with Raspberry Pi 4 Model B. This is just a case, Raspberry Pi Board is NOT INCLUDED
- Can be mounted to flat surfaces, DIN rails, or to any object via zip ties
- Cases can be stacked to form clusters of single board computers
- Supports 25*25mm fans from 5mm to 10mm tall. Fans can be attached directly into the case body, or using the provided fan adapter (FAN NOT INCLUDED)
- Comes with two covers - Low Profile cover when hosting just the single board computer and Automation cover for extra space when hosting extension boards with larger relays, terminals and connectors
- Workload and purpose: Decide whether the goal is Kubernetes practice, application hosting, network processing, or a high-capacity integrated system. That determines whether many small nodes, expandable mini PCs, or specialized DPUs make sense.
- Architecture compatibility: Identify the CPU architectures in the cluster and check that container images, system packages, and network components support each one.
- Memory and storage: Match RAM and local or shared storage to the workload. Account for the board’s actual interfaces and for any PCIe slot or enclosure constraints.
- Network capacity: Size switching and node interfaces for the workload rather than comparing headline aggregate bandwidth alone. Confirm port count, speed, and any PoE needs.
- Power and cooling: PoE can reduce separate power wiring for compatible Pi nodes, but requires suitable HATs and a switch with an adequate power budget. More capable x86 systems may offer expansion at higher power consumption, as the mixed-lab author notes.
- Enclosure and mounting: Mixed SBCs can be difficult to fit together because vendors use different form factors. Arm Community’s miniNodes article describes a universal SBC mounting plate and a 4U rack-mountable design; current availability and program status are not established here.
- Manageability and total cost: Consider cabling, power supplies, switch capacity, storage, cooling, physical access, and ongoing administration—not just the price of the compute boards. The cited sources do not provide a current, independently verified price comparison between Pi nodes and x86 mini PCs.
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