CSD2-N128 AI Server

$124,499.00

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Spec:QCS8550 QCS8550 RK3588S RK3576 BM1688 K3

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CSD2-N128

CSD2-N128 features 16 built-in compute blades (128 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

CSD2-N128 offers a wide range of AI processor options, including mainstream hardware platforms such as Qualcomm, Rockchip, SOPHGO, and SpacemiT, adapting to diverse computing scenarios.

All-Module Hot-Swap Design

The server adopts fully hot-swappable modular architecture, integrating BMC network modules, switch network modules, power modules, and 16 compute blade modules. Each compute blade is standardly equipped with 8 independent compute nodes, enabling high-density deployment of up to 128 compute nodes.

Android Multi-Instance Support

Adopts Android container-level isolated multi-instance architecture. Through lightweight virtualization scheduling mechanism, it efficiently reuses SoC hardware resources, significantly improving chip computing power and overall system resource utilization.

80 Gbps Aggregate Peak Bandwidth

Equipped with 8 x 10GbE SFP+ ports, delivering an aggregate peak bandwidth of up to 80 Gbps to meet high-bandwidth application requirements. An independent BMC management network interface separates the management network from the business network, ensuring secure and reliable network communication.

Granular Network Security Control

Supports granular dynamic network isolation between core modules, enabling flexible control of cross-board communication permissions. It also features network traffic control, bandwidth scheduling, and Layer 3 network policy configuration, allowing on-demand network domain partitioning and business traffic isolation to ensure comprehensive network communication security and data exchange reliability.

Smaller Failure Impact Range

Each compute blade module is equipped with 8 core boards. When a single core board fails, maintenance affects only its corresponding 8 compute nodes — significantly better than the industry norm of 16-20 affected nodes.

3840-Channel AI Video Processing

Supports AI processing for up to 3,840 video streams (actual processing performance may vary depending on SoM specifications and type of AI model being run). With multi-task concurrent processing capabilities, it is widely applicable in AI application scenarios such as intelligent security and edge computing.

Efficient and Low-Cost

Highly integrates computing units, storage, USB controllers, network controllers, power management controllers, and sensors into a single system. It provides an all-in-one SDK for deep learning development, along with a suite of software tools including low-level drivers, compilers and inference deployment tools, reducing users' procurement, development and operational costs.

128 Computing Nodes, Powerful Performance

Fully configured system can deploy up to 128 compute nodes, with each node delivering 6-60 TOPS of computing power. Each node can support 5-10 containers deployed in parallel based on business requirements, enabling the entire system to virtualize and host 640-1,280 system containers. This effectively revitalizes hardware resources and significantly improves overall resource utilization efficiency.

Application Scenarios

Widely applicable in industry fields such as Edge Computing, Large Model Localization, Smart City, Smart Healthcare, Smart Industry and Intelligent Security.

Edge Computing

Private Deployment of Large Models

Smart City

Smart Healthcare

Smart Industry

Intelligent Security

Interfaces

Specifications

		CSD2-N128Q8550	CSD2-N128R3576	CSD2-N128R3588S	CSD2-N128SPK3
Technical Specifications	Server form	2U rack-mounted computing power server
	Architecture	ARM architecture			RISC-V architecture
	Number of nodes	16 compute blades (128 distributed compute nodes) + 1 control node
	Compute nodes	Hexa-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
	Control nodes	Octa-core 64-bit processor RK3588, main frequency up to 2.4GHz, the highest computing power is 6TOPS
	AI computing power	6144TOPS (48T × 128, INT8)	768TOPS (6T × 128, INT8)		7680TOPS (60T× 128, INT8)
	Fan module	14 high-speed cooling fans
Basic Specifications	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
	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
	RAM	16GB LPDDR5X × 128	8GB LPDDR4/LPDDR5 × 128 (4/8/16GB)	16GB LPDDR5 × 128 (4/8/16/32GB)	32GB LPDDR5 × 128 (8/16/32GB)
	Storage	256GB UFS4.0 × 128	64GB eMMC × 128 (16/32/64/128/256GB)	256GB eMMC × 128 (16/32/64/128/256GB)	128GB UFS2.2 × 128
	Power	2 × 1300W hot-swappable power supplies, 1+1 redundancy support
	Dimension	495.60mm × 928.52mm × 88.80mm
	Environment	Operating Temperature: 0ºC ~ 30ºC, Storage Temperature: -40ºC ~ 60ºC, Operating Humidity: 5% ~ 80%RH (non-condensing)
Physical Specifications	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
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	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.
	Vision large models	K3: Supports private deployment of all vision large models QCS8550: Supports private deployment of vision large models including Qwen2.5-VL, InternVL3, etc.
	AI Painting	K3: Supports private deployment of all image generation models QCS8550: Supports private deployment of the Stable Diffusion image generation model
	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
Interface Specifications	Internet	8 × 10Gbps 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 × VGA (maximum resolution 1080P, BMC management display)
	USB	3 × USB3.0, 1 × Type-C (OTG)
	Button	1 × Power, 1 × UID, 1 × Recovery, 1 × Reset