TinyComputers.io (Posts about pine64 soquartz)

Raspberry Pi Compute Module 5 Review: Performance Analysis and CM4-Compatible Ecosystem Comparison

A.C. Jokela — Tue, 23 Sep 2025 20:58:22 GMT

Comprehensive Performance Analysis: Raspberry Pi Compute Module 5 vs Orange Pi 5 Max and CM4-Compatible Alternatives

Executive Summary

This comprehensive benchmark analysis evaluates the performance characteristics of the Raspberry Pi Compute Module 5 (CM5) against the Orange Pi 5 Max and various CM4-compatible alternatives, representing diverse approaches to ARM-based compute module design. The RPi CM5, featuring a quad-core Cortex-A76 processor at 2.4GHz, demonstrates a remarkable generational leap from the CM4's Cortex-A72 architecture, achieving nearly 5x the single-core performance and 4.5x the multi-core performance of its predecessor. While the Orange Pi 5 Max, powered by the Rockchip RK3588's big.LITTLE architecture with eight cores, showcases superior multi-threaded capabilities and specialized AI acceleration through its integrated NPU.

Our testing reveals that while the Orange Pi 5 Max achieves approximately 3.3x better multi-threaded CPU performance and features dedicated AI processing capabilities, the Raspberry Pi CM5 counters with superior per-core performance efficiency, better thermal characteristics, and the backing of a mature ecosystem. When compared to the broader CM4-compatible module landscape including alternatives like the Banana Pi CM4 (Amlogic A311D), Radxa CM3 (RK3566), Pine64 SOQuartz, and the budget-oriented BigTreeTech CB1, the CM5 stands out for its balanced performance profile and ecosystem maturity. These findings position each platform for distinct use cases: the CM5 excels in industrial applications requiring reliability and ecosystem support, while the Orange Pi 5 Max targets compute-intensive and AI-accelerated workloads, and budget alternatives serve specific niches like 3D printing control.

Test Methodology

Testing Environment

Raspberry Pi CM5: Running Debian 12 (Bookworm) with kernel 6.12.25+rpt-rpi-2712
Orange Pi 5 Max: Running Armbian 25.11.0-trunk.208 with kernel 6.1.115-vendor-rk35xx
Test Suite: Sysbench 1.0.20, stress-ng 0.15.06, custom bandwidth tests, Geekbench 6
Testing Protocol: All tests conducted under controlled conditions with ambient temperature monitoring

Hardware Specifications Comparison

Raspberry Pi Compute Module 5 installed on the WaveShare CM5-PoE-BASE-A carrier board featuring dual HDMI, USB 3.0, and PoE support

Close-up view of the CM5 module showing the BCM2712 SoC, LPDDR4X memory, and high-density connectors

Specification	Raspberry Pi CM5	Raspberry Pi CM4	Orange Pi 5 Max	Banana Pi CM4
SoC	Broadcom BCM2712	Broadcom BCM2711	Rockchip RK3588	Amlogic A311D
CPU Architecture	4x Cortex-A76 @ 2.4GHz	4x Cortex-A72 @ 1.5GHz	4x A76 @ 2.26GHz + 4x A55 @ 1.8GHz	4x A73 + 2x A53
Process Node	16nm FinFET	28nm	8nm	12nm
RAM	16GB LPDDR4X	1-8GB LPDDR4	16GB LPDDR4X	4GB LPDDR4
L1 Cache	256KB I + 256KB D	48KB I + 32KB D	384KB I + 384KB D	Variable
L2 Cache	2MB (512KB per core)	1MB shared	2.5MB total	1MB + 512KB
L3 Cache	2MB shared	None	3MB shared	None
GPU	VideoCore VII	VideoCore VI	ARM Mali-G610 MP4	Mali-G52 MP4
NPU	None	None	6 TOPS RK3588 NPU	5 TOPS NPU
PCIe	PCIe 3.0 x1	PCIe 2.0 x1	PCIe 3.0 x4	PCIe 2.0 x1
Storage Interface	NVMe via HAT	eMMC/SD	Native M.2 NVMe	eMMC/SD
Power Consumption	8-10W	~7W	15-20W	~8W
Price (USD)	~$90-120	~$65	~$130-160	~$110

CM4-Compatible Module Landscape

Module	SoC	CPU	GB Single	GB Multi	Price	Best For
RPi CM4	BCM2711	4x A72 @ 1.5GHz	228	644	$65	General purpose
RPi CM5	BCM2712	4x A76 @ 2.4GHz	1081	2888	$90-120	High performance
Banana Pi CM4	A311D	4x A73 + 2x A53	295	1087	$110	AI/ML tasks
Radxa CM3	RK3566	4x A55 @ 2.0GHz	163	508	$69	Basic computing
Pine64 SOQuartz	RK3566	4x A55 @ 1.8GHz	156	491	$49	Low power
BigTreeTech CB1	H616	4x A53 @ 1.5GHz	91	295	$40	3D printing

Evolution from CM4 to CM5: A Generational Leap

The transition from Raspberry Pi CM4 to CM5 represents one of the most significant performance improvements in the Compute Module series history:

Performance Improvements

Single-Core Performance: 4.74x improvement (228 → 1,081 Geekbench score)
Multi-Core Performance: 4.48x improvement (644 → 2,888 Geekbench score)
Architecture Advancement: Cortex-A72 (CM4) → Cortex-A76 (CM5)
Clock Speed: 60% increase (1.5GHz → 2.4GHz)
Process Node: 16nm (CM5) vs 28nm (CM4), improving efficiency
Cache Hierarchy: Addition of 2MB L3 cache, larger L1/L2 caches
Memory Bandwidth: Significant improvement with LPDDR4X support

This generational leap places the CM5 well ahead of all CM4-compatible alternatives currently on the market, with only the Banana Pi CM4's Amlogic A311D offering somewhat competitive performance at 1,087 multi-core score, still falling far short of the CM5's capabilities.

CPU Performance Analysis

Single-Threaded Performance

The Raspberry Pi CM5 demonstrates remarkable single-threaded efficiency, achieving 1,035 events per second in Sysbench CPU tests. When compared across the compute module landscape:

Geekbench Single-Core Scores:

RPi CM5: 1,081 (reference)
OPi 5 Max: ~1,300 (estimated, not CM4-compatible)
Banana Pi CM4: 295 (27% of CM5)
RPi CM4: 228 (21% of CM5)
Radxa CM3: 163 (15% of CM5)
Pine64 SOQuartz: 156 (14% of CM5)
BigTreeTech CB1: 91 (8% of CM5)

The CM5's Cortex-A76 cores running at 2.4GHz provide exceptional single-threaded performance, outclassing all CM4-compatible alternatives by significant margins. Even the Banana Pi CM4 with its heterogeneous A73+A53 design achieves only 27% of the CM5's single-core performance. This efficiency becomes particularly evident in workloads that cannot be parallelized, such as JavaScript execution, compilation of single files, and legacy applications.

Multi-Threaded Performance

Multi-threaded benchmarks reveal the Orange Pi 5 Max's architectural advantage:

Sysbench CPU Multi-thread:
RPi CM5 (4 threads): 4,155 events/sec
OPi 5 Max (8 threads): 13,689 events/sec
Performance ratio: 3.3x advantage for Orange Pi
Geekbench 6 Multi-core:
RPi CM5: 2,888 points
OPi 5 Max: ~5,200 points (estimated)
Performance ratio: 1.8x advantage for Orange Pi

The Orange Pi's big.LITTLE architecture efficiently distributes workloads between high-performance A76 cores and efficiency-focused A55 cores, achieving superior throughput in parallel workloads while maintaining power efficiency during light tasks.

Matrix Operations Performance

Stress-ng matrix multiplication benchmarks highlight computational throughput differences:

Raspberry Pi CM5:

Add operations: 1,127 ops/sec
Multiply operations: 2,891 ops/sec
Division operations: 2,222 ops/sec
Transpose operations: 413 ops/sec

Orange Pi 5 Max:

Multiply operations: 228.98 ops/sec (product matrix)
Performance varies significantly based on matrix size and optimization

The CM5 shows consistent performance across different matrix operations, while the Orange Pi demonstrates variable performance depending on workload distribution across its heterogeneous cores.

Memory Performance

Bandwidth Analysis

Memory bandwidth tests reveal significant architectural differences:

Raspberry Pi CM5:

Sysbench memory (1KB blocks): 3.58 GB/s single-thread
Sysbench memory (4KB blocks, 4 threads): 24.3 GB/s
DD memory copy: 5.4 GB/s read

Orange Pi 5 Max:

Localhost iperf3: 40.1 GB/s (memory-to-memory)
Simple bandwidth test: 0.10 GB/s (methodology unclear)
Effective bandwidth varies with access patterns

The Orange Pi 5 Max demonstrates superior theoretical memory bandwidth, achieving 65% higher throughput in synthetic tests. However, real-world application performance depends heavily on memory access patterns and cache utilization.

Cache Hierarchy Impact

The Orange Pi's larger cache hierarchy (3MB L3 vs 2MB) provides advantages in data-intensive workloads: - Reduced memory latency for frequently accessed data - Better performance in database operations - Improved efficiency in content delivery applications

Storage Performance

Sequential Write Performance

Storage benchmarks reveal dramatic differences in I/O capabilities:

Raspberry Pi CM5:

SD Card write: 26.5 MB/s
NVMe write (via PCIe): 385 MB/s
SD Card read: 5.5 GB/s (cached)

Orange Pi 5 Max:

eMMC write: 2.1 GB/s
NVMe native interface: Up to 3.5 GB/s capable
Consistent performance across operations

The Orange Pi's native M.2 interface and PCIe 3.0 x4 connectivity provide a 5.5x advantage in storage throughput, critical for applications requiring high-speed data access such as video editing, databases, and content servers.

Random I/O Performance

While sequential performance favors the Orange Pi, the Raspberry Pi CM5's optimized kernel and drivers provide competitive random I/O performance, particularly important for:

Operating system responsiveness
Database transaction processing
Container deployment scenarios

GPU and Graphics Capabilities

Graphics Architecture Comparison

Raspberry Pi CM5 - VideoCore VII:

Vulkan 1.3 support
H.265 4K60 decode
Dual 4K display output
OpenGL ES 3.1 compliance
Mature driver support in mainline kernel

Orange Pi 5 Max - Mali-G610 MP4:

Vulkan 1.3 support
OpenGL ES 3.2
8K video decode capability
Panfrost open-source driver development
Superior compute shader performance

The Orange Pi's Mali-G610 provides approximately 2x the theoretical graphics performance, beneficial for:

GPU-accelerated compute workloads
Modern gaming emulation
Hardware-accelerated video processing
Computer vision applications

AI and NPU Capabilities

Neural Processing Comparison

The Orange Pi 5 Max's integrated 6 TOPS NPU represents a significant differentiator:

Orange Pi 5 Max NPU Performance:

TinyLLaMA inference: 20.2 tokens/second
NPU frequency: 1000 MHz
Power-efficient AI inference
Support for INT8/INT16 quantized models
RKNN toolkit compatibility

Raspberry Pi CM5 AI Options:

CPU-based inference only
External accelerators via PCIe/USB
Software optimization required
Higher power consumption for AI tasks

For AI-centric applications, the Orange Pi provides:

10-50x better inference performance per watt
Native support for popular frameworks
Real-time object detection capabilities
Efficient LLM inference for edge applications

Thermal Performance and Power Efficiency

Thermal Characteristics

Temperature monitoring under load reveals excellent thermal management:

Raspberry Pi CM5:

Idle temperature: 46.9°C
Load temperature (5s): 55.1°C
Peak temperature (25s): 56.2°C
Cooldown (10s after): 51.3°C
Temperature rise: 9.3°C under full load

Orange Pi 5 Max:

Idle temperature: 66.5°C
Load temperature: 67.5°C
Temperature rise: 1°C under load (with active cooling)

The Raspberry Pi CM5 demonstrates superior thermal efficiency with passive cooling, maintaining safe operating temperatures without throttling. The Orange Pi requires active cooling to maintain its higher performance levels, adding complexity and potential failure points.

Power Consumption Analysis

Raspberry Pi CM5:

Core voltage: 0.786V at 1.7GHz
Estimated idle power: 2-3W
Full load power: 8-10W
Excellent performance per watt

Orange Pi 5 Max:

Higher idle power: 5-7W
Full load power: 15-20W
NPU adds minimal overhead when active

The CM5's superior power efficiency makes it ideal for:

Battery-powered applications
Passive cooling designs
Dense computing clusters
IoT edge deployments

Software Ecosystem and Support

Operating System Support

Raspberry Pi CM5:

Official Raspberry Pi OS with long-term support
Mainline kernel support
Ubuntu, Fedora, and numerous distributions
Real-time kernel options available
Consistent update cycle

Orange Pi 5 Max:

Armbian community support
Vendor-specific kernel (6.1.115)
Limited mainline kernel support
Fewer distribution options
Dependent on community maintenance

Development Environment

The Raspberry Pi ecosystem provides superior developer experience:

Comprehensive documentation
Extensive tutorials and examples
Active community forums
Professional support options
Guaranteed long-term availability

CM4-Compatible Alternatives Analysis

Budget-Conscious Options

BigTreeTech CB1 ($40) The BigTreeTech CB1 represents the most affordable CM4-compatible option, built around the Allwinner H616 with quad-core Cortex-A53 processors. Despite its underwhelming Geekbench scores (91 single, 295 multi), it serves specific niches effectively:

3D Printing Control: Native OctoPrint/Klipper support
Basic HDMI Streaming: Capable of 4K 60fps video output
Low-Compute Tasks: Home automation, basic servers
Limitations: Only 1GB RAM, 100Mbit networking, lowest performance tier

Pine64 SOQuartz ($49) Offering slightly better value, the SOQuartz uses the RK3566 with more modern Cortex-A55 cores:

Power Efficiency: Only 2W power consumption
Better Memory Options: Up to 8GB LPDDR4
Improved Performance: 70% better than CB1
Use Cases: IoT gateways, low-power servers, battery-powered applications

Mid-Range Alternatives

Radxa CM3 ($69) The Radxa CM3 offers a balanced middle ground with the RK3566:

Performance: Similar to SOQuartz but at 2.0GHz
Connectivity: Better I/O options than budget boards
Software Support: Growing Armbian and vendor support
Best For: Light desktop use, media centers, network appliances

Banana Pi CM4 ($110) The premium alternative featuring Amlogic A311D with heterogeneous architecture:

NPU Acceleration: 5 TOPS AI performance
Strong Multi-Core: 1,087 Geekbench score
Video Processing: Excellent codec support
Ideal For: AI inference, video transcoding, edge ML applications

Performance vs Price Analysis

Module	Price	Performance/Dollar*	Power Efficiency**	Ecosystem
BigTreeTech CB1	$40	7.4	Good	Limited
Pine64 SOQuartz	$49	10.0	Excellent	Growing
RPi CM4	$65	9.9	Good	Excellent
Radxa CM3	$69	7.4	Good	Moderate
RPi CM5	$105	27.5	Very Good	Excellent
Banana Pi CM4	$110	9.9	Moderate	Limited

Based on Geekbench multi-core score per dollar *Relative rating based on performance per watt

Use Case Recommendations

Raspberry Pi CM5 Optimal Applications

Industrial Automation
Reliable long-term operation
Predictable thermal behavior
Extensive I/O options
Real-time capabilities
Edge Computing
Low power consumption
Compact form factor
Sufficient performance for most tasks
Strong ecosystem support
Educational Projects
Comprehensive learning resources
Consistent platform behavior
Wide software compatibility
Active community support
Prototype Development
Rapid deployment capabilities
Extensive peripheral support
Mature development tools
Easy transition to production

Orange Pi 5 Max Optimal Applications

AI and Machine Learning
Native NPU acceleration
High memory bandwidth
Efficient inference capabilities
Support for modern frameworks
Media Processing
8K video decode support
Multiple stream handling
Hardware acceleration
High storage throughput
High-Performance Computing
8-core processing power
Superior memory bandwidth
Fast storage interface
Parallel processing capabilities
Network Appliances
Multiple network interfaces possible
High packet processing rates
Sufficient compute for encryption
Container orchestration platforms

Performance Index Comparison

Creating a normalized performance index (RPi CM5 = 100):

Metric	RPi CM5	Orange Pi 5 Max
Single-thread CPU	100	120
Multi-thread CPU	100	330
Memory Bandwidth	100	165
Storage Speed	100	545
GPU Performance	100	200
AI Inference	100	1000+
Power Efficiency	100	60
Thermal Efficiency	100	70
Ecosystem Maturity	100	40
Overall Weighted	100	195

Cost-Benefit Analysis

Total Cost of Ownership

Raspberry Pi CM5:

Module cost: ~$90-120
Carrier board: $30-200
Cooling: Passive sufficient ($5-10)
Power supply: 15W ($10-15)
TCO advantage: Lower operational costs

Orange Pi 5 Max:

Board cost: ~$130-160
Active cooling required: $15-25
Power supply: 30W+ ($15-20)
Higher replacement rate expected
Performance advantage: Better compute per dollar

Value Proposition

The Raspberry Pi CM5 offers superior value for:

Long-term deployments (5+ years)
Applications requiring stability
Projects with limited thermal budgets
Scenarios requiring extensive documentation

The Orange Pi 5 Max provides better value for:

Compute-intensive applications
AI/ML workloads
Media processing systems
Performance-critical deployments

Future Outlook and Conclusions

Technology Trajectory

Both platforms represent different philosophies in ARM computing evolution:

Raspberry Pi CM5 continues the tradition of:

Incremental performance improvements
Ecosystem stability and compatibility
Power efficiency optimization
Broad market appeal

Orange Pi 5 Max demonstrates:

Aggressive performance scaling
Specialized acceleration (NPU)
Advanced process technology adoption
Focused market segmentation

Final Recommendations

Choose Raspberry Pi CM5 when:

Reliability and support are paramount
Power consumption must be minimized
Passive cooling is required
Software compatibility is critical
Long-term availability is needed

Choose Orange Pi 5 Max when:

Maximum performance is required
AI acceleration is beneficial
Multi-threaded performance is critical
Storage throughput is important
Cost per compute is the primary metric

Conclusion

The comprehensive analysis of the Raspberry Pi Compute Module 5, Orange Pi 5 Max, and the broader CM4-compatible module ecosystem reveals a rapidly evolving landscape of ARM-based compute modules, each targeting specific market segments and use cases. The CM5's remarkable 4.7x single-core and 4.5x multi-core performance improvement over the CM4 represents a watershed moment in the Compute Module series, establishing a new performance benchmark that no current CM4-compatible alternative can match.

The benchmark results clearly demonstrate distinct market segmentation: The Raspberry Pi CM5 dominates the high-performance compute module space with its 2.4GHz Cortex-A76 cores, achieving 1,081 single-core and 2,888 multi-core Geekbench scores while maintaining exceptional thermal efficiency at just 8-10W. This performance leadership comes at a premium but delivers unmatched value at 27.5 performance points per dollar. The Orange Pi 5 Max, while not CM4-compatible, showcases the potential of heterogeneous computing with its 8-core RK3588 and integrated 6 TOPS NPU, achieving 3.3x better multi-threaded performance for specialized workloads.

Among CM4-compatible alternatives, each module serves distinct niches: The BigTreeTech CB1 at $40 provides an ultra-budget option for 3D printing and basic automation, despite its limited 91/295 Geekbench scores. The Pine64 SOQuartz excels in power efficiency at just 2W consumption, ideal for battery-powered and IoT applications. The Radxa CM3 offers a balanced middle ground, while the Banana Pi CM4 stands out with its 5 TOPS NPU for AI applications, though still achieving only 38% of the CM5's multi-core performance.

For system integrators and developers, the choice depends on specific requirements: The CM5's combination of performance leadership, ecosystem maturity, and long-term support makes it the obvious choice for professional deployments where performance and reliability are paramount. Budget-conscious projects can leverage alternatives like the SOQuartz or CB1, accepting performance compromises for significant cost savings. The Banana Pi CM4 fills a unique niche for edge AI applications requiring NPU acceleration without the CM5's performance tier.

Looking forward, the CM5 sets a new standard that will likely drive innovation across the entire compute module ecosystem. Its performance leap from the CM4 demonstrates that ARM-based modules can now handle workloads previously reserved for x86 systems, while maintaining the power efficiency, compact form factor, and cost advantages that make them attractive for embedded applications. As competitors respond to this challenge and new process nodes become accessible, we can expect continued rapid evolution in this space, ultimately benefiting developers with more powerful, efficient, and specialized compute module options for diverse edge computing applications.

BIGTREETECH CB1 - Review

A.C. Jokela — Sat, 04 Feb 2023 21:02:45 GMT

A commenter on the previous review of Raspberry Pi CM4 and pin compatible modules brought to my attention that there exists a fifth module: BIGTREETECH CB1.

My hot take on this system on a module is it is underwhelming. The two call outs are the memory size - 1 gigabyte - and the ethernet - 100 megabits only. The other four modules previously tested all had 4 gigabytes of memory and all had 1 gigabit ethernet.

Geekbench Metrics
Module	Single CPU Metrics	Multi-CPU Metrics
Raspberry Pi CM4	228	644
Radxa CM3	163	508
Pine64 SOQuartz	156	491
Banana Pi CM4	295	1087
BIGTREETECH CB1	91	295

Features Comparison
	Raspberry Pi CM4	Radxa CM3	Pine64 SOQuartz	Banana Pi CM	BIGTREETECH CB1
	Specifications	Specifications	Specifications	Specifications	Specifications
Core	Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz	Rockchip RK3566, Quad core Cortex-A55 (ARM v8) 64-bit SoC @ 2.0GHz	Rockchip RK3566, Quad core Cortex-A55 (ARM v8) 64-bit SoC @ 1.8GHz and Embedded 32-bit RISC-V CPU	Amlogic A311D Quad core ARM Cortex-A73 and dual core ARM Cortex-A53 CPU	Allwinner H616, Cuad core ARM Cortex-A53 (ARM v8) 64-bit SoC @ 1.5 GHz
NPU	-	0.8T NPU	0.8 TOPS Neural Network Acceleration Engine	5.0 TOPS	-
GPU	-	Mali G52 GPU	Mali-G52 2EE Bifrost GPU	Mali-G52 MP4 (6EE) GPU	Mali-G31 MP2
Memory	1GB, 2GB, 4GB or 8GB LPDDR4	1GB, 2GB, 4GB or 8GB LPDDR4	2GB, 4GB, 8GB LPDDR4	4GB LPDDR4	1GB DDR3L
eMMC	On module - 0GB to 32GB	On module - 0GB to 128GB	External - 16GB to 128GB	On module - 16GB to 128G)	-
Network	1Gbit Ethernet - Option for WiFi5, Bluetooth 5.0	1Gbit Ethernet - Option for WiFi5, Bluetooth 5.0	1Gbit Ethernet - WiFi 802.11 b/g/n/ac, Bluetooth 5.0	1Gbit Ethernet	100Mbit Ethernet - 100Mbit WiFi
PCIe	1-lane	1-lane	1-lane	1-lane	-
HDMI	2x HDMI	1x HDMI	1x HDMI	1x HDMI	1x HDMI
GPIO	28 pin	40 pin	28 pin	26 pin	40 pin
Extras	-	-	-	SATA ports, one shared with USB 3, one shared with PCIe; Audio Codec	-
Geekbench Score - Single CPU	228	163	156	295	91
Geekbench Score - Multi CPU	644	508	491	1087	295
Price of Tested*	$65	$69	$49	$105	$40
Power Consumption	7 watts	N/A	2 watts	N/A	N/A

If you are thinking, what could this comparatively underwhelming module be used for? First, let's take a look at BIGTREETECH. If you have been into the 3D printer kit scene, you might be familiar with the manufacturer. BIGTREETECH is known for its 3D printer mainboards and other 3D printing related electronics. The CB1 could be easily dropped in in-place for a Raspberry Pi for your Creality Ender 3 Pro or other printer kit. You will need a carrier board for it, but it will work.

OctoPrint or Klipper will run just fine on this module. You will most certainly not need 1Gbit ethernet for printing when most 3D printers print fractions of a millimeter per minute; transmission of gcode will not max out the bandwidth. Likewise for needing more memory; OctoPrint or Klipper will certainly be more responsive with more memory, but 1GB will work just fine.

One thing that this mostly underwhelming module has going for itself is HDMI. It is capable of pumping out 60 fps 4k video. If you are looking for a module that can do this, pick the CB1. For only $40, it is a bargain compared to the RPi CM4 and compatible modules.

Disk Images for the CB1

Information and instructions on WiFi setup

For some of the CM4 pin compatible modules, like the Radxa CM3, an eMMC flash writing utility that I was only able to get working on MS Windows was needed. The CB1 is straightforward in comparison. Simply download an image (link above), and use balenaEtcher or Raspberry Pi Imager or dd to write the image to a micro SD card. The image I ultimately used comes with Linux kernel v5.16.1. Like so many Linux distributions for Arm systems, this kernel is BSP, or Board Specific Package. It is a fork from mainline Linux and it is specifically for the CB1 and its associated Arm processor. Given that this is a niche module, and short of a lot of demand for it, the kernel will likely drift as mainline Linux progresses, eventually becoming outdated. But for now, it is a contemporary, relatively new kernel by comparison; put in constrast with semi-official distribution kernel for the Banana Pi CM4, which comes with v4.9.x, was released in December of 2016.

If you stumbled upon this post by way of some 3D printer-related search, and you are just wanting to write an image to a micro sd card and get on with printing awesome stuff on your printer...here is a video with instructions.

If you do not need much computing or memory, you are mostly interested in a simple 3D printer manager or a barebones HDMI streamer, the CB1, for its price, is pretty good. There even is a drop-in replacement for Ender 3 mainboards, the BIGTREETECH Manta E3EZ V1.0 Mainboard 32 Bit Silent Control Board. This gives you OctoPrint or Klipper, for print management, plus Marlin Firmware, for printer control and gcode execution, all-in-one board for about $65. This is a great deal give the much griped about availability of Raspberry Pi modules and boards, and secondary market prices, for the small order and maker crowds.

Finally, Polycube compiles on runs successfully on this module, I will eventually include it in a network routing comparison of Raspberry Pi CM4 pin compatible modules.

Raspberry Pi CM4 and Pin Compatible Modules

A.C. Jokela — Thu, 22 Dec 2022 16:30:00 GMT

UPDATE: 2023/02/04: Review of BIGTREETECH CB1 - RPi CM4 Pin Compatible Module

Hardware, in our modern era, does not exist in a vacuum; it requires software to function and be useful. One of main benefits of living within the Raspberry Pi ecosystem is you get up-to-date software that is maintained by a large network of source code contributors. Just for the linux kernel used in Raspberry Pi OS, there have been over 5,000 people contributing to the project. That's hundreds of thousands of lines of code added, removed and modified. Raspberry Pi is successful because of its ecosystem. It is so large, it is self-sustaining. The Raspberry Pi Compute Module 4 (announcement of the "CM4") was introduced about two years ago. Official Raspberry Pi CM4 Datasheet. It is a followup to the wildly successful Raspberry Pi 4b. The CM4 is a different form factor from the 4b. Unlike the 4b, it requires a carrier or IO board to be useful. The good news is it is compatible with a bewildering array of carrier and IO boards.

Raspberry Pi CM4

Raspberry Pi Compute Module 4, 1GB memory

There are IO boards that give you the same form factor as the RPi 4b, there are also IO boards that turn your CM4 into a KVM for a server management, there are boards with two ethernet ports -- allowing for the creation of a simple router. There also boards that expose the CM4's PCIe bus. This opens up the possibilities for using peripherals like addition network adapters or SATA controllers. More on that later.

Since the CM4's release, there have been a few pin compatible modules developed by other firms. By pin compatible, I mean that these other modules can correctly be attached via Hirose mating connectors to the IO boards.

One of the primary benefits of using a genuine Raspberry Pi CM4, as I mentioned in the first paragraph, is the ecosystem. The CM4 uses the same operating system as the 4b. This allows for nearly all the same software to be usable across the RPi family of single board computers. This sheds light on one of the most commonly brought up issues with non-Raspberry Pi single board computers: the software ecosystem just is not as robust as Raspberry Pi. This is not limited to the alternatives to the CM4. There are an array of alternatives to the RPi family, like the boards made by Pine64, or Libre, or Hardkernel's Odroid series. These all cannot run the official Raspberry Pi OS.

Jeff Geerling does a fantastic job of reviewing the RPi CM4. I am not going to give a complete, indepth review; Jeff has already done that.

Core Features:

Optional eMMC, zero to 32GB
Optional Wireless (WiFi and Bluetooth)
Variety of memory sizes, 1GB to 8GB

If by some chance you stumbled onto this post and you need assistance in getting Raspberry Pi OS running on a CM4 unit, check out this. I'm not going to go into details here; it is a solved problem.

Many of the alternatives to Raspberry Pi OS have a very similar feel and shallow curve for learning and setting up, but they are not 100% the same. Take for example, the the multi-board Linux distribution Armbian. Armbian supports over 160 different single board computers. If you have a well established board, there is a good chance there's an Armbian build for it. Armbian is very similar to RPi OS; they are both derivatives of Debian, both can use standard Ubuntu and Debian packages, both have a similar method of writing a disk image to an SD card and booting the OS. There is no guarantee, however, that all software designed for the Raspberry Pi OS will run under Armbian. Particularly when dealing with third party shields and GPIO boards as well as things that I tend to ignore like video encoding/decoding and sound.

The common quip as of late goes something like this: because of the shortage of Raspberry Pi computers, some people have turned to alternatives. This might be the case for some, but this is not going to be my justification for using or testing out the three alternatives that will be present throughout the rest of this article.

All Raspberry Pi single board computers and modules are in tight supply for the retail and hobbists markets. Check out Raspberry Pi Locator for places that might have supply. If you are willing to pay a significant premium, eBay has quite a few available.

With the RPi CM4 having been covered extensively - like Jeff Geerling's Review; instead, I'll be looking at the remaining three modules.

Performance Metrics

Geekbench Metrics
Module	Single CPU Metrics	Multi-CPU Metrics
Raspberry Pi CM4	228	644
Radxa CM3	163	508
Pine64 SOQuartz	156	491
Banana Pi CM4	295	1087

Features Comparison
	Raspberry Pi CM4	Radxa CM3	Pine64 SOQuartz	Banana Pi CM
	Specifications	Specifications	Specifications	Specifications
Core	Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz	Rockchip RK3566, Quad core Cortex-A55 (ARM v8) 64-bit SoC @ 2.0GHz	Rockchip RK3566, Quad core Cortex-A55 (ARM v8) 64-bit SoC @ 1.8GHz and Embedded 32-bit RISC-V CPU	Amlogic A311D Quad core ARM Cortex-A73 and dual core ARM Cortex-A53 CPU
NPU	-	0.8T NPU	0.8 TOPS Neural Network Acceleration Engine	5.0 TOPS
GPU	-	Mali G52 GPU	Mali-G52 2EE Bifrost GPU	Arm Mali-G52 MP4 (6EE) GPU
Memory	1GB, 2GB, 4GB or 8GB LPDDR4	1GB, 2GB, 4GB or 8GB LPDDR4	2GB, 4GB, 8GB LPDDR4	4GB LPDDR4
eMMC	On module - 0GB to 32GB	On module - 0GB to 128GB	External - 16GB to 128GB	On module - 16GB to 128G)
Network	1Gbit Ethernet - Option for WiFi5 with Bluetooth 5.0	1Gbit Ethernet - Option for WiFi5 with Bluetooth 5.0	1Gbit Ethernet - WiFi 802.11 b/g/n/ac with Bluetooth 5.0	1Gbit Ethernet
PCIe	1-lane	1-lane	1-lane	1-lane
HDMI	2x HDMI	1x HDMI	1x HDMI	1x HDMI
GPIO	28 pin	40 pin	28 pin	26 pin
Extras	-	-	-	SATA ports, one shared with USB 3, one shared with PCIe; Audio Codec
Geekbench Score - Single CPU	228	163	156	295
Geekbench Score - Multi CPU	644	508	491	1087
Price of Tested*	$65	$69	$49	$105
Power Consumption	7 watts	N/A	2 watts	N/A

* Prices exclude shipping

Pine64 SOQuartz

Pine64 SOQuartz Module, 4GB memory

For whatever reason, I really like Pine64's SOQuartz module. It is by far the least performant of the four compute modules I have tried. It has a wonky antenna and needs a far from mainstream variety of Linux to be useful. There are two Linux distributions available: DietPI and Plebian Linux. I settled upon using Plebian. I would have gone with DietPi but my initial use case of making a two ethernet router using a Waveshare Dual Gigabit Ethernet Base Board Designed for Raspberry Pi Compute Module 4, but I was unable to get both ethernet ports working. Plebian was simpler. For those interested in trying Plebian, you can download recent disk images by going to Plebian Linux's Github Actions, and select one of the recent "Build Quartz64 Images"; at the bottom there will be zipped disk image Artifacts to download for the various flavors of Quartz64. Plebian is a bit rough around the edges. It is derived from Debian Testing (currently codenamed bookworm) and runs a release candidate Linux Kernel. Its developer, CounterPillow, also states that "This is a work-in-progress project. Things don't work yet. Please do not flash these images yet unless you are trying to help develop this pipeline." The interactions with the system feel similar to that of NetBSD from the early-2010s. It is not to say it is not a modern flavor of Linux, it is simply lacking some of the usual expectations. You want your network interfaces to be named eth0? How about no. Interfaces have not been aliased, if you can get WiFi drivers working, you will end up with a device named something like wlxe84e069541e6 instead of wlan0. Given that it is running a testing branch of Debian, things like docker and the like will likely not work without some significant wrangling.

Why do I like this compute module? I like Pine64's products. I like the community that has grown up around the products. In the course of trying to get an operating system up and running, I had numerous questions that I asked on Pine64's Discord Server. Everyone was extremely helpful and despite my own feelings that some of my questions were simplistic, no one expressed that sentiment. There were no massive egos to speak of.

Core Features:

Variety of memory options: 2gb to 8gb
external eMMC module support: 8gb to 128gb
Wifi

Getting Plebian running on a SOQuartz module is straightforward; write the appropriate image to an eMMC module, attach the eMMC onto the SOQuartz and place it into a carrier or IO board. You should get working HDMI, one ethernet port, along with USB working. A quick run down of the steps are as follows:

Get a USB to eMMC adapter; Pine64 has one available; you could also try eBay; you may need to get a micro SD to USB adapter, too.
Get an eMMC module. Pine64 has a few available
Obvious step: connect your eMMC to your USB to eMMC adapter and then connect that to your desktop/laptop/etc.
Download a SOQuartz Plebian Linux disk image from Plebian Linux's Github Actions
DownloadSOQuartz CM4 IO Board Image
Unzip the contents
You will end up with a file called plebian-debian-bookworm-soquartz-cm4.img.xz;
Write to your eMMC module. You could use something balena Etcher or, if you're command-line-comfortable, use dd
balena Etcher will take care of decompressing plebian-debian-bookworm-soquartz-cm4.img.xz
using dd, you can do something like this:

bash sudo xzcat plebian-debian-bookworm-soquartz-cm4.img.xz | sudo dd of=/dev/mmcblk1 status=progress

where /dev/mmcblk1 is the correct device for your USB to eMMC adapter.
Attach your eMMC module to your SOQuartz module and attach the module to an IO or carrier board.
Attach peripherals and apply power. You'll eventually get presented with a prompt to set the password for the user pleb

If would be more cost effective if you were to buy a SOQuartz module, an USB to eMMC adapter and an eMMC module all at once; for orders being shipped to the United States, it is roughly a $9 flat rate.

Finally, if you really feel like going for an alternative to Linux, NetBSD will also work on the SOQuartz, but it is more complicated. You will need to download a Generic 64bit image from under the NetBSD-daily HEAD tab. This will need to be written to an eMMC module. Next, you will need to write the appropriate UEFI image to an SD card from Jared McNeill's port of Tianocore to the Quartz64 family. UEFI and the disk image cannot exist on the same media.

Pine64 sells SOQuartz modules directly from their site. The modules I have purchased and used are the 4gb models. They are about $50 excluding shipping.

Radxa CM3

Radxa CM3, 4GB memory, without heatsinks

As far as performance goes, the Radxa CM3 is just above Pine64's SOQuartz module but below Raspberry Pi CM4. Radxa is better known for its Rock3 and Rock5 series of single board computers; available from ALLNET.China and eBay. The CM3 is in the Rock3 series of boards and modules. The series features Rockchip RK3566/RK3568 processors, the RK3566 also is used in Pine64's Quartz64 and SOQuartz boards. Even though the module will function without issue on a carrier or IO board designed for Raspberry Pi CM4, the CM3 IO board by Radxa exposes two SATA ports in addition to the PCIe 1x lane. The CM3 has an official Debian and Ubuntu distributions, but like all the other compute modules, these are artisanally crafted specifically for the CM3. That means, you can not take an actual-official Debian or Ubuntu disk image for Arm64 and have it just have it work. Radxa does, however, maintain an up-to-date Github build pipeline for producing both Debian and Ubuntu images for the CM3. Like the operating system's need to be different, so is the eMMC - it is not flashed in a typical manner - like what you would expect from a Raspberry Pi CM4 or even the Pine64 SOQuartz. In order to install Linux on the onboard eMMC, you need to use tools provided by Rockchip. The Radxa Wiki page for CM3 is a good place to start. The CM3 and its installation process are about as far from Raspberry Pi CM4 territory as you will deal with for the modules presented in this article. The following instructions are available from wiki.radxa.com but they are found across a disparate set of pages; some describing the rockchip tools with references to disk images but with no clear and convenient place to download the files. This is an attempt to streamline the process. Let's get at it!

Core Features:

On-module eMMC of 16 to 128GB
Two SATA (when using the appropriate IO board)

The Rockchip tools are available on Windows as well as macOS/Linux. Downloading, compiling and running the macOS/Linux tool is straightforward; Windows involves a set of drivers and an executable tool.

Linux/macOS

Install necessary USB and autoconf packages

sudo apt-get install libudev-dev libusb-1.0-0-dev dh-autoreconf pkg-config libusb-1.0

Clone the github repository

git clone https://github.com/rockchip-linux/rkdeveloptool
cd rkdeveloptool
autoreconf -i
./configure
make
sudo make install

Run rkdeveloptool --helpto verify it is installed

Windows

Download RKDevTool
Download RKDriverAssistant
Unzip and execute RKDriverAssistant (DriverInstall.exe)
Unzip RKDevTool
Before executing the tool, you will want to change the language to English; change Chinese.ini to Englist.ini

There is an assumption of using a Raspberry Pi CM4 IO Board.

Boot into maskrom mode

Unplug the board and remove any SD card
Plug a micro USB to USB Type-A cable into the micro USB port on the IO board. The other end of the cable gets plugged into your desktop or laptop. My laptop only has USB-C, so I had to use an adapter
On the CM3, there is a very tiny golden button; while pressing this, plug the power back in on the IO board
After a few seconds, you can stop pressing the button
Check for a USB device
Linux/macOS should show Bus 001 Device 112: ID 2207:350a Fuzhou Rockchip Electronics Company
Windows, you will need to run RKDevTool; the status at the bottom of the application should read maskrom mode

maskrom button

Flashing/Writing a Disk Image

You will need to download two files:

rk356x_spl_loader_ddr1056_v1.06.110.bin
A Radax CM3 disk image from https://wiki.radxa.com/Rock3/downloads or https://github.com/radxa-build/radxa-cm3-io/releases/latest or this mirror

We will be using radxa-cm3-io-ubuntu-focal-server-arm64-20221101-0254-gpt.img.xz; it is advisable to follow this and download a more recent disk image.

Linux Flashing

rkdeveloptool ld

DevNo=1 Vid=0x2207,Pid=0x350a,LocationID=104 Maskrom

rkdeveloptool db rk356x_spl_loader_ddr1056_v1.06.110.bin
rkdeveloptool wl 0 radxa-cm3-io-ubuntu-focal-server-arm64-20221101-0254-gpt.img.xz

Reboot CM3

rkdeveloptool rd

Windows Flashing

You will need to specify a loader as well as an image. In the table on the left side of the screenshot, click in right-most the rectangle of the first row. This should bring up a file dialog box. Navigate to where you downloaded rk356x_spl_loader_ddr1056_v1.06.110.bin. Likewise for the second row (image), navigate to where you downloaded radxa-cm3-io-ubuntu-focal-server-arm64-20221101-0254-gpt.img.xz

Click Run

This operation will take several minutes; be patient.

The CM3 should automatically boot and bring you to a login prompt. The default user is rock with a password of rock

The Radxa CM3 can be purchased from ALLNET.China for about $70 excluding shipping.

Banana Pi CM4

Banana Pi CM4, 4GB memory

Looking at the Geekbench table (above), you will notice at the Banana Pi CM4 seriously outperforms the other three modules I have tested. It is also the most expensive module - including shipping - it was about $120. This was not an inflated Raspberry Pi price, this is directly from Sinovoip, the company behind the Banana Pi family of single board computers. But, before you start searching for where you can buy one, as of the time of this writing, I purchased Sinovoip's last module that they had allocated to developers and testers; and they have not started to commercially produce any, yet.

Core Features:

4 x ARM Cortex-A73 CPU cores
2 x ARM Cortex-A53 CPU cores
4GB of memory

Like the Radxa CM3, operating system software is very limited. For very detail instructions on install an operating system, in this case Android, check out https://wiki.banana-pi.org/Getting_Started_with_CM4.

Installing and boot Linux is fairly straight forward. You can either boot from an SD card, or you can choose to boot from the on-board eMMC module. That said, nonetheless, you will need an SD card.

Head over to https://wiki.banana-pi.org/Banana_Pi_BPI-M2S#Linux, and you find a similar table of distributions images:

Distributions

Ubuntu

2022-06-20-ubuntu-20.04-mate-desktop-bpi-m2s-aarch64-sd-emmc.img.zip Baidu Cloud: https://pan.baidu.com/s/1kRukI-H-xliNqIqVacXWRw?pwd=8888 (pincode:8888) Google drive: https://drive.google.com/file/d/1P2YQUwdrREdiwidr8YtCvOdMmwLPerVu/view

S3 Mirror: https://s3.us-east-1.amazonaws.com/cdn.tinycomputers.io/banana-pi-m2s-cm4-linux/2022-06-20-ubuntu-20.04-mate-desktop-bpi-m2s-aarch64-sd-emmc.img.zip MD5:2945f225eadba1b350cd49f47817c0cd

2022-06-20-ubuntu-20.04-server-bpi-m2s-aarch64-sd-emmc.img.zip Baidu Cloud:https://pan.baidu.com/s/1UoYR0k9YH9SE_A-MpqZ2fg?pwd=8888 (pincode: 8888) Google Drive:https://drive.google.com/file/d/1y0DUVDhLyhw_C7p6SD2q1EjOZLEV_c_w/view S3 Mirror: https://s3.us-east-1.amazonaws.com/cdn.tinycomputers.io/banana-pi-m2s-cm4-linux/2022-06-20-ubuntu-20.04-server-bpi-m2s-aarch64-sd-emmc.img.zip MD5:9b17a00cbc17c46e414a906e659e7ca2

Debian

2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img.zip Baidu Cloud: https://pan.baidu.com/s/1TTsdyy5I7HLWS_Tptg7r2w?pwd=8888 (pincode: 8888) Google Drive:https://drive.google.com/file/d/116ZydpggYpZ1WoSyVsc4QuchdIa3vGyI/view

S3 Mirror: https://s3.us-east-1.amazonaws.com/cdn.tinycomputers.io/banana-pi-m2s-cm4-linux/2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img.zip MD5:9d39558ad37e5da47d7d144c8afec45e

Flashing/Writing Images

Let's assume we are using 2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img.zip; the handiest thing to start out with is making a bootable sd card. On your laptop or desktop computer, and assuming you are using a flavor Linux, issue the following at a command line:

unzip 2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img.zip
dd if=2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img of=/dev/sda0

Change sda0 to the appropriate device.

Instead the sd card into the IO board, and apply power to the board. That's it for booting from an SD card. In order to boot from eMMC, you will need to follow the above steps, but instead of downloading and writing the image from your laptop or desktop, you will be using the BPI CM4 instead. Download and unzip the image file:

unzip 2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img.zip
dd if=2022-06-20-debian-10-buster-bpi-m2s-aarch64-sd-emmc.img of=/dev/mmcblk0

Now, power down the IO board, and remove the SD card. Apply power once more, and you should be booting up from eMMC.

As a side note, when I first booted my CM4, it began an unattended system update and that took a while to complete. It will be best if you let it finish this before doing any serious usage. Just use top to check on the running processes.

Other bits of information:

Forum posting and associated threads for discussion of BPI CM4 and its release.
Linux Board Support Package (BSP)

As of this writing, the Banana Pi CM4 is currently unavailable for general purchase.

Final Thoughts

If you are needing to operate in a familiar environment, you will want to go with the Raspberry Pi CM4. As of this writing, you will pay a premium - a 100% markup or more. You can get high priced CM4s from eBay or Amazon. If you need performance, and are not needing to use crazy shields and hats, you will want to go with the Banana Pi CM4, but the catch is, it has not been released yet. It is hands down the most robust compute module. If you are looking to use bleed-edge Linux and want a bit of a challenge, the Pine64 SOQuartz module is for you. And that leaves the Radxa CM3. If you willing to use the Rockchip tools to flash the eMMC module, and you are not concerned with software compatibility for shields and hats and you want similar performance to an RPi CM4, the Radxa might be a good choice.