Shixinda AI’s AI Accelerator Card (48 TOPS)
Shixinda AI’s AI Accelerator Card (48 TOPS)
Old Servers + Shixinda AI Core: Insert Shixinda AI accelerator card into existing servers to upgrade the computing power of monitoring centers without replacing the entire server. With 48 TOPS peak computing power, up to 90% computing power utilization rate, and broad compatibility with mainstream servers based on X86 and ARM architectures, it meets the requirements for large-scale inference transformation of clusters.
Specifications
| ### Hardware Parameters | |
|---|---|
| | Item | Specification | | |
| | --- | --- | | |
| | Interface Form | PCIe Card | | |
| | AI Engine | 72-core AI Inference Engine | | |
| | Peak Computing Power | 48 TOPS | | |
| | Computing Power Utilization | Up to 90% | | |
| | Compatible Platforms | Mainstream Servers with X86/ARM Architectures | |
Product Overview: Legacy Servers + AI Brain
Shixinda AI’s AI Accelerator Card (48 TOPS) is designed for computing power upgrade and transformation of existing servers. Existing X86/ARM servers in monitoring centers do not need to be replaced; inserting this accelerator card directly delivers 48 TOPS AI computing power, enabling video intelligent analysis capabilities for legacy monitoring systems. It is a low-cost, quick-to-implement solution for central computing power transformation.
Upgrade & Transformation Values
- Reuse Entire Server: Upgrade by directly inserting the card into existing servers, no need to replace the entire unit.
- Rapid Deployment: Standard PCIe interface, plug-and-play.
- Cluster Expansion: Add cards on demand, computing power expands smoothly with the scale of transformation.
- Compatible with Mainstream Servers: Widely compatible with X86 and ARM architectures.
Core Features
- High Performance: 48 TOPS peak computing power, multi-core AI inference engine.
- High Utilization: Up to 90% end-to-end computing power utilization, with high actual measured computing power.
- Cluster Inference: Supports large-scale AI inference applications on server clusters.
Typical Transformation Scenarios
Computing power upgrade for existing server resources in monitoring centers, large-scale video inference transformation for clusters, and other relevant scenarios.
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