CSD2-N128

Launch in April 2026

2U rack-mounted computing power server

ARM architecture

16 compute blades (128 distributed compute nodes) + 1 control node

Octa-core 64-bit processor Qualcomm QCS8550, up to 3.36GHz

Octa-core 64-bit processor RK3588S2, up to 2.4GHz

Octa-core 64-bit processor RK3576, up to 2.2GHz

Octa-core 64-bit processor SpacemiT Key Stone K3, up to 2.4GHz

8K@30fps/4K@120fps H.264

H.264: 1×8K@30fps, 16×1080P@30fps

H.264: 1×4K@60fps

4K@60fps H.264

8K@60fps/4K@240fps H.264/VP9/AV1

8K@60fps/4K@120fps (VP9/AVS2) 8K@30fps (H.264/AVC/MVC) 30×1080P@30fps (H.264)

1×4K@120fps (VP9,AVS2,AV1) 1×4K@60fps (H.264/AVC)

4K@120fps H.264/VP9

Octa-core 64-bit processor RK3588, main frequency up to 2.4GHz, the highest computing power is 6TOPS

6144TOPS (48T × 128，INT8)

768TOPS (6T × 128，INT8)

7680TOPS (60T× 128，INT8)

16GB LPDDR5X × 128

16GB LPDDR5 × 128 (4/8/16/32GB)

8GB LPDDR4/LPDDR5 × 128 (4/8/16GB)

32GB LPDDR5 × 128 (8/16/32GB)

256GB UFS4.0 × 128

256GB eMMC × 128 (16/32/64/128/256GB)

64GB eMMC × 128 (16/32/64/128/256GB)

128GB UFS2.2 × 128

2 × 1300W hot-swappable power supplies, 1+1 redundancy support (Input: 100V AC ~ 240V AC)

7 high-speed cooling fans

495.60mm(L) × 928.52mm(W) × 88.80mm(H)

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

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

Operating Temperature: 0ºC ~ 30ºC, Storage Temperature: -40ºC ~ 60ºC, Operating Humidity: 5% ~ 80%RH (non-condensing)

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

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.

K3: Supports private deployment of all vision large models QCS8550: Supports private deployment of vision large models including Qwen2.5-VL, InternVL3, etc.

K3: Supports private deployment of all vision large models QCS8550: Supports private deployment of vision large models including Qwen2.5-VL, InternVL3, etc.

K3: Supports private deployment of all image generation models QCS8550: Supports private deployment of the Stable Diffusion image generation model

K3: Supports private deployment of all image generation models QCS8550: Supports private deployment of the Stable Diffusion image generation model

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

8 × 10Gbps SFP+, 1 × Gigabit Ethernet (RJ45, MGMT is used as BMC management network)

1 × Console (RJ45, BMC debug serial port, baud rate 115200)

1 × VGA (maximum resolution 1080P, BMC management display)

3 × USB3.0, 1 × Type-C (OTG)

1 × Power, 1 × UID, 1 × Recovery, 1 × Reset