· Mainstream SoC Platform Support
· All-Module Hot-Swap Design
· Android Multi-Instance Support
· 80 Gbps Aggregate Peak Bandwidth
· Granular Network Security Control
· Smaller Single-Point Failure Impact Range
· 3 pcs of 3.5-inch SATA3.0 HDD/SSD
· Highly Integrated Server Design
Gemma
Llama
Qwen
Stable Diffusion
CSD2-N96 features 12 built-in compute blades (96 compute nodes in total), with each node delivering 6-60 TOPS of computing power. Platform options include Qualcomm, Rockchip, SOPHGO and SpacemiT. It supports private deployment of mainstream AI large models and multiple deep learning frameworks. Equipped with 8 x 10GbE ports, achieving peak switch bandwidth of 80 Gbps. With standard 2U rackmount server chassis design, it also comes with intelligent BMC management system.
Mainstream SoC Platform Support
All-Module Hot-Swap Design
Android Multi-Instance Support
80 Gbps Aggregate Peak Bandwidth
Granular Network Security Control
Smaller Failure Impact Range
3 pcs of 3.5-inch SATA3.0 HDD/SSD
Efficient and Low-Cost
Fully configured system can deploy up to 96 compute nodes, with each node supporting 5-10 containers deployed in parallel based on business requirements. The entire system can virtualize and host 480-960 system containers, effectively revitalizing hardware resources and significantly improving overall resource utilization efficiency.
Widely applicable in industry fields such as Edge Computing, Large Model Localization, Smart City, Smart Healthcare, Smart Industry and Intelligent Security.

CSD2-N96Q8550 |
CSD2-N96R3576 |
CSD2-N96R3588S |
CSD2-N96SPK3 |
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Technical Specifications |
Server form |
2U rack-mounted computing power server |
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Architecture |
ARM architecture |
RISC-V architecture |
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Number of nodes |
12 compute blades (96 distributed compute nodes) + 1 control node |
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Compute nodes |
Octa-core 64-bit processor Qualcomm QCS8550, up to 3.36GHz |
Octa-core 64-bit processor RK3576, up to 2.2GHz |
Octa-core 64-bit processor RK3588S, up to 2.4GHz |
Octa-core 64-bit processor SpacemiT Key Stone K3, up to 2.4GHz |
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Video encoding |
8K@30fps/4K@120fps H.264 |
H.264: 1×4K@60fps |
H.264: 1×8K@30fps, 16×1080P@30fps |
4K@90fps H.264 |
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Video decoding |
8K@60fps/4K@240fps H.264/VP9/AV1 |
1×4K@120fps (VP9,AVS2,AV1) 1×4K@60fps (H.264/AVC) |
8K@60fps/4K@120fps (VP9/AVS2) 8K@30fps (H.264/AVC/MVC) 30×1080P@30fps (H.264) |
4K@180fps H.264/VP9 |
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Control nodes |
Octa-core 64-bit processor RK3588, main frequency up to 2.4GHz, the highest computing power is 6TOPS |
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AI computing power |
4608TOPS (48T × 96, INT8) |
576TOPS (6T × 96, INT8) |
5760TOPS (60T× 96, SquareINT4) |
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RAM |
16GB LPDDR5X × 96 |
8GB LPDDR4/LPDDR5 × 96 (4/8/16GB) |
16GB LPDDR5 × 96 (4/8/16/32GB) |
32GB LPDDR5 × 96 (8/16/32GB) |
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Storage |
256GB UFS4.0 × 96 |
64GB eMMC × 96 (16/32/64/128/256GB) |
256GB eMMC × 96 (16/32/64/128/256GB) |
128GB UFS2.2 × 96 |
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Storage expansion |
3.5-inch/2.5-inch SATA3.0 SSD hard drive slot × 3 (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) |
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Power |
2 × 1300W hot-swappable power supplies, 1+1 redundancy support |
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Fan module |
14 high-speed cooling fans |
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Physical Specifications |
Size |
Standard 2U rack servers: 495.60mm × 928.51mm × 88.80mm |
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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 |
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Environment |
Operating Temperature: 0ºC ~ 30ºC, Storage Temperature: -40ºC ~ 60ºC, Operating Humidity: 5% ~ 80%RH (non-condensing) |
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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 |
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Large language models |
All models support private deployment of ultra-large-scale parameter models under the Transformer architecture, such as large language models including Deepseek-R1 Series, Gemma Series, Llama Series, ChatGLM Series, Qwen Series, Phi Series, etc. |
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Visual large model |
K3: Supports private deployment of all vision large models QCS8550: Supports private deployment of vision large models including Qwen2.5-VL, InternVL3, etc. |
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AI Painting |
K3: Supports private deployment of all image generation models QCS8550: Supports private deployment of the Stable Diffusion image generation model |
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Deep learning |
All models: Support traditional network architectures such as CNN, RNN, LSTM, and support various deep learning frameworks such as TensorFlow, PyTorch, PaddlePaddle, ONNX, and Caffe. Support custom operator development and Docker containerization management technology |
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Interface Specifications |
Internet |
8 × 10Gbps SFP+, 1 × Gigabit Ethernet (RJ45, MGMT is used as BMC management network) |
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Console |
1 × Console (RJ45, BMC debug serial port, baud rate 115200) |
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Display |
1 × VGA (maximum resolution 1080P, BMC management display) |
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USB |
3 × USB3.0, 1 × Type-C (OTG) |
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Button |
1 × Power, 1 × UID, 1 × Recovery, 1 × Reset |
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