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NVIDIA

NVIDIA 900-53669-0000-000 | NVLink Bridge 3-Slot 2-Way, RTX A-Series Compatible

SKU:900-53669-0000-000

Stock Status: Enquire

$282.10 inc. GST
$256.45 ex. GST
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Description

The NVIDIA 900-53669-0000-000 is a high-speed NVLink bridge designed to connect two RTX A-series professional GPUs with 3-slot spacing. This interconnect enables GPU-to-GPU memory pooling and performance scaling, delivering up to 112 GB/s of total bandwidth for AI, deep learning, simulation, and professional visualization workloads. Compatible with RTX A6000, A5500, A5000, A4500, A40, and select datacenter GPUs.

Features

High-speed GPU-to-GPU interconnect delivering up to 112 GB/s total bandwidth
- Third Generation NVLink technology for NVIDIA Ampere Architecture GPUs
- Enables memory pooling across two GPUs for unified memory access
- Supports performance scaling for AI, deep learning, and HPC workloads
- Direct GPU-to-GPU communication with significantly lower latency than PCIe
- x16 NVLink interface for maximum bandwidth between professional GPUs
- 3-slot physical spacing design for standard workstation and server configurations
- Compatible with NVIDIA RTX A-series professional visualization GPUs
- Supports dual-GPU configurations for rendering, simulation, and AI inference
- Physical bridge design ensures reliable high-speed connectivity
- Enables real-time collaboration on large-scale 3D models and datasets
- Supports unified memory addressing for simplified application development

Warranty

All products sold by XS Network Tech include a 12-month warranty on both new and used items. Our in-house technical team thoroughly tests used hardware prior to sale to ensure enterprise-grade reliability.

All technical data should be verified on the manufacturer data sheets.

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Technical Specifications

FAQs

Technical Specifications

Product Type: NVLink Bridge Interconnect
- Configuration: 2-way dual-GPU bridge
- Slot Spacing: 3-slot (3 PCI-E slot spacing)
- Interface: x16 NVLink high-speed interconnect
- Total Bandwidth: Up to 112 GB/s (single bridge)
- Compatible GPUs: NVIDIA RTX A6000, A5500, A5000, A4500, A40, GeForce RTX 3090, A100 (requires 3 bridges), A800, H100
- Architecture Support: NVIDIA Ampere Architecture (Third Generation NVLink)
- Part Number: 900-53669-0000-000
- Alternative Part Numbers: P3669, NVLAMP-3SLOT-BSP

FAQs

Q: What GPUs are compatible with this 3-slot NVLink bridge?
A: This bridge is compatible with NVIDIA RTX A6000, A5500, A5000, A4500, A40 professional GPUs, GeForce RTX 3090, and datacenter GPUs including A30, A100, A800, and H100. Both connected GPUs must be the same model. For A100 40GB/80GB configurations, three NVLink bridges are required for full connectivity.

Q: What is the bandwidth advantage of using NVLink over PCIe?
A: NVLink provides significantly higher bandwidth than traditional PCIe connections, delivering up to 112 GB/s of total bandwidth with a single bridge between two GPUs. This enables faster GPU-to-GPU data transfer, memory pooling across cards, and improved performance scaling for compute-intensive workloads like AI training, rendering, and simulation.

Q: What does '3-slot spacing' mean for this bridge?
A: The 3-slot spacing indicates that this bridge is designed for systems where the two connected GPUs are separated by 3 PCI-E slot positions. The physical bridge length is engineered to span this distance. It's essential to verify your system's GPU slot spacing matches before ordering—2-slot bridges are also available for tighter GPU configurations.

Q: Can I use this bridge to connect different GPU models?
A: No, both GPUs connected by the NVLink bridge must be identical models. You cannot mix different GPU models (for example, an RTX A6000 with an A5000, or an A-series card with a GeForce RTX 3090). The GPUs must also both support NVLink functionality.

Q: What workloads benefit most from NVLink connectivity?
A: NVLink excels in workloads requiring large memory capacity and high GPU-to-GPU data transfer rates, including AI model training with large datasets, real-time ray tracing and rendering, CAD/CAE simulation, deep learning inference, data science analytics, and professional visualization applications that exceed single-GPU memory capacity.

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