| CSB1-N10NOrinNano |
Technical Specifications
|
Server form |
1U rack-mounted computing power server
|
| Framework |
ARM architecture
|
| Number of
nodes |
10 distributed computing nodes (up to 80 ARM cores) + 1 control node
|
| Compute nodes |
Hexa-core 64-bit processor NVIDIA Jetson OrinNano, main frequency up to 1.5GHz
|
| Control nodes |
Octa-core 64-bit processor RK3588, main frequency up to 2.4GHz, the highest computing power is 6TOPS
|
| AI computing
power |
400TOPS (INT8)
|
| RAM |
8GB LPDDR5 × 10 (Number of compute nodes)
|
| Storage |
256GB (2242 PCIe NVMe SSD, the server is internally assembled)
|
| Storage
Expansion |
3.5-inch/2.5-inch SATA3.0/SSD hard drive slot × 1 (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)
|
| Fan module |
6 high-speed cooling fans
|
Physical Specifications
|
Size |
420.0mm(L) × 421.3mm(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: 8.1kg, total weight with packaging: 10.3kg
|
| Environment |
Operating Temperature: 0ºC ~ 45ºC, Storage Temperature: -40ºC ~ 60ºC, Operating Humidity: 5% ~ 90%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 model |
Robot model: ROS robot model is supported.
Large language models: Support Ollama local large model deployment framework, which can be used for natural language
processing, code generation, and assistance scenarios. Support the private deployment of ultra-
large-scale parametric models under the Transformer architecture, such as LLaMa3 and Phi-3 Mini.
Large visual models: Support the privatization deployment of large visual models such as EfficientVIT, NanoOWL, NanoSAM,
SAM and TAM.
AI Painting: Support ComfyUI graphical deployment framework, which can be used for scenarios such as image restoration,
image style conversion, and image synthesis. Supports the private deployment of Flux, Stable Diffusion and Stable Diffusion XL
image generation model in the AIGC field.
|
| Deep learning |
Supports multiple deep learning frameworks accelerated by cuDNN, including PaddlePaddle, PyTorch, TensorFlow, MATLAB, MxNet, Caffe2, Chainer and Keras.
Supports custom operator developmen.
Docker containerization: Docker containerization technology is supported, which can be easily used for image deployment.
|
Interface Specifications
|
Internet |
2 × 10G Ethernet (SFP+), 2 × Gigabit Ethernet (RJ45), 1 × Gigabit Ethernet (RJ45, MGNT is used as BMC management network)
|
| Console
|
1 × Console (RJ45, BMC debug serial port, baud rate 115200)
|
| Display
|
1 × VGA (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 button
|
| Other interfaces
|
1 × RS232 (DB9, baud rate 115200),1 × RS485 (DB9, baud rate 115200)
|
