· RISC-V Processor SG2042
· Deployment of Large AI Models
· Multiple Deep Learning Frameworks
· Features 12 Drive Bays for Large Storage Capacity
· Secure & High-Speed Network Communication
· Supports privacy computing technologies
· Standard 2U rack server size
· Supports Multiple Operating Systems
Gemma
Qwen
Llama
Phi
Powered by a single high-performance RISC-V processor SG2042, equipped with 64 64-bit cores, delivering powerful performance and supporting mixed-precision computing. It supports private deployment of mainstream AI large models and a variety of deep learning frameworks. It features 12 drive bays for 3.5-inch / 2.5-inch SAS / SATA hard drives and 2 5G Ethernet ports. It comes with an intelligent management system and supports mainstream privacy computing technologies such as homomorphic encryption and differential privacy. The design adopts a standard 2U rackmount server chassis.
RISC-V Processor SG2042
Private Deployment
Deep Learning Frameworks
Ultra-Large Storage Capacity
Secure Network Communication
Privacy Computing Technologies
Standard 2U Rack Server Size
Multiple Operating Systems
Based on the international open-source RISC-V instruction set, there is no authorization restriction, and it supports mainstream operating systems, creating a truly open computing platform and making innovation ubiquitous.
Widely applicable in industry fields such as private deployment of large models, deep learning, cloud storage, ecosystem development, telecommunications, and privacy computing.

RS-F1SG2042 |
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Technical Specifications |
Server form factor |
2U rackmount computing server |
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Architecture |
RISC-V architecture |
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Processor |
1 × SG2042 64-core RISC-V processor @2.0GHz |
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Computing precision |
INT8/FP16/BF16/FP32 |
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Memory |
4 × DDR4 DIMM slots, supporting a maximum frequency of 3200MHz, maximum single module capacity of 64GB, and a maximum total memory capacity of 64×4=256GB |
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Expanded storage |
12 × 3.5"/2.5" SATA3.0 SSD drive bays (Hot-swappable; BMC enables direct hard drive operation, and computing nodes can access hard drives indirectly via the network sharing provided by BMC) |
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PCIe expansion slots |
4 × GEN4 X16 PCIe Slots, 4 × GEN4 X8 PCIe Slots |
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Power supply |
2 × 550W Hot-swappable power supplies, supporting 1+1 redundancy (Input: 100V AC ~ 240V AC) |
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Fan module |
4 × Hot-swappable fans, supporting N+1 redundancy |
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Physical Specifications |
Size |
481mm(L) × 677mm(W) × 88mm(H) |
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Weight |
Net server weight: 15kg, Gross weight with packaging: 17.2kg |
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Environment |
Operating Temperature: 0ºC ~ 35ºC, Storage Temperature: -40ºC ~ 60ºC, Operating Humidity: 5% ~ 80%RH (Non-condensing) |
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Software Specifications |
BMC |
Integrated BMC management system with web-based UI, supporting redfish, VNC, NTP, advanced monitoring and virtual media; the BMC management system supports secondary development |
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Large language models |
Supports on-premises deployment of ultra-large parameter models based on Transformer architecture, such as Deepseek- R1 Series, Gemma Series, Llama Series, ChatGLM Series, Qwen Series and other LLMs. |
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Vision large models |
Supports on-premises deployment of vision large models such as ViT, Grounding DINO, SAM. |
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AI Painting |
Supports on-premises deployment of image generation models including Flux, Stable Diffusion, Stable Diffusion XL. |
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Deep learning |
Supports traditional network architectures such as CNN, RNN, LSTM; compatible with multiple deep learning frameworks including TensorFlow, PyTorch, PaddlePaddle, ONNX, Caffe; supports custom operator development and Docker containerization management technology. |
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Interface Specifications |
Ethernet |
2 × 5Gbps Ethernet (RJ45), 1 × Gigabit Ethernet (RJ45, MGMT for BMC management network) |
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Display |
2 × VGA (Maximum resolution 1080P, for BMC management display) |
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USB |
4 × USB3.0, 1 × Type-C (BMC debug serial port, built-in UART chip, baud rate 115200) |
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Button |
1 × Reset, 1 × UID, 1 × Power |
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