· Private deployment of AI models
· Multiple deep learning frameworks
· 8K video decoding, 4K video encoding
· Supports 3.5-inch SATA3.0 HDD/SSD
· 4-port 10 Gigabit SFP+ network interface
· Standard 1U rack server size
· Includes aBMC management system
· Highly integrated server design
Llama
Qwen
Stable Diffusion
CSB1-N4AGXOrin can be configured with up to 4 NVIDIA Jetson AGX Orin (64 GB) compute modules, delivering a maximum computing power of 1100 TOPS (INT8). It supports the private deployment of mainstream AI models and various deep learning frameworks. Equipped with four 10-gigabit network ports, it also allows for expansion with SATA 3.0 hard drives.
Private Deployment
Deep Learning Frameworks
8K Decoding & 4K Encoding
3.5-inch SATA 3.0 HDD/SSD
4-port 10 Gigabit SFP+
Standard 1U Rack Server Size
aBMC Management System
Efficient and Low-Cost
Configured with up to four NVIDIA Jetson AGX Orin (64GB) compute nodes, delivering a peak performance of 1100 TOPS (INT8) to provide robust computing power for AI and deep learning applications.
Widely applicable in fields such as edge computing, large model localization, smart cities, smart healthcare, smart industry, and smart agriculture.

CSB1-N4AGXOrin |
||
|
Technical Specifications |
Server form |
1U rack-mounted computing power server |
|
Architecture |
ARM architecture |
|
|
Number of nodes |
4 distributed computing nodes + 1 control node |
|
|
Compute nodes |
12-core 64-bit processor NVIDIA Jetson AGX Orin (64GB), main frequency up to 2.2GHz |
|
|
Video encoding |
2×4K@60fps, 4×4K@30fps, 8×1080P@60fps, 16×1080P@30fps |
|
|
Video decoding |
1×8K@30fps, 3×4K@60fps, 7×4K@30fps, 11×1080P@60fps, 22×1080P@30fps |
|
|
Control nodes |
Octa-core 64-bit processor RK3588, main frequency up to 2.4GHz, the highest computing power is 6TOPS |
|
|
AI computing power |
1100TOPS (275T × 4, INT8) |
|
|
RAM |
64GB LPDDR5 × 4 |
|
|
Storage |
64GB eMMC × 4 |
|
|
Storage expansion |
3.5-inch/2.5-inch SATA3.0 SSD hard drive slot × 1 (Supports hot swapping; BMC can directly operate the hard drive, and computing child nodes can indirectly access the hard drive through the network sharing method provided by BMC) |
|
|
Power |
550W AC power supply (Input: 90V AC~264V AC, 47 Hz~63 Hz, 8A; Hot swappable not supported) |
|
|
Power consumption |
Normal: 135W, Max: 400W |
|
|
Fan module |
6 high-speed cooling fans |
|
|
Physical Specifications |
Size |
494.0mm(L) × 469.3mm(W) × 44.4mm(H) |
|
Installation requirements |
IEC 297 Universal Cabinet Installation: 19 inches wide and 800 mm deep and above Retractable slideway installation: The distance between the front and rear holes of the cabinet is 543.5mm~848.5mm |
|
|
Full weight |
Server net weight: 8.8kg, total weight with packaging: 11kg |
|
|
Environment |
Operating Temperature: 0ºC ~ 40ºC, Storage Temperature: -40ºC ~ 60ºC, Operating Humidity: 5% ~ 80%RH (non-condensing) |
|
|
Software Specifications |
BMC |
The BMC management system is integrated with the web-based management interface, supporting Redfish, VNC, NTP, monitoring advanced and virtual media, and the BMC management system can be redeveloped. |
|
Large language models |
Support the privatization deployment of ultra-large-scale parametric models under the Transformer architecture, such as Deepseek-R1 series, Gemma series, Llama series, ChatGLM series, Qwen series, Phi series and other large language models. |
|
|
Visual large model |
Support the privatization deployment of large visual models such as EfficientVIT, NanoOWL, NanoSAM, SAM and TAM. |
|
|
AI Painting |
Supports the private deployment of Flux, Stable Diffusion, and Stable Diffusion XL image generation models. |
|
|
Deep learning |
Support Ollama local large model deployment framework and ComfyUI graphical deployment framework. Support for a variety of deep learning frameworks supported by cuDNN acceleration, including PaddlePaddle, PyTorch, TensorFlow, MATLAB, MXNet, Keras. Support custom operator development and Docker containerization management technology. |
|
|
Interface Specifications |
Internet |
4 × 10G Ethernet (SFP+), 1 × Gigabit Ethernet (RJ45, MGMT is used as BMC management network) |
|
Console |
1 × Console (RJ45, BMC debug serial port, baud rate 115200) |
|
|
Display |
1 × HDMI (maximum resolution 1080P, BMC management display) |
|
|
USB |
2 × USB3.0 (The lower USB is USB3.0 OTG, and the BMC can be upgraded OTG by using a USB flash drive) |
|
|
Button |
1 × Reset, 1 × UID, 1 × Power |
|
|
Other interfaces |
1 × RS232 (DB9, baud rate 115200),1 × RS485 (DB9, baud rate 115200) |
|
Firefly team, with over 20 years of experience in product design, research and development, and production, provides you with services such as hardware, software, complete machine customization, and OEM server.