Solution AI Server QSFP

QSFP-DD, QSFP112, and QSFP56 transceivers provide scalable, high-bandwidth connectivity for AI servers, enabling efficient GPU cluster interconnects at 100G, 200G, 400G, and beyond.

QSFP Form Factors for AI Servers

QSFP-DD (Quad Small Form-factor Pluggable – Double Density) supports up to 400G using 8 high-speed electrical lanes and is backward compatible with 100G and 200G transceivers, making it ideal for AI data centers transitioning to higher bandwidth networks . It is widely used in spine-leaf architectures to handle east-west traffic between GPU servers, ensuring low latency and high throughput . QSFP112 is a high-performance transceiver supporting 100G per lane, allowing flexible configurations such as 100G, 200G, and 400G. It is particularly effective for connecting AI GPUs (e.g., NVIDIA H100/A100) to 400G network fabrics and supports breakout configurations (4x100G) to maximize scalability in high-density AI clusters . QSFP56 provides 200G connectivity using four 50G PAM4 lanes. It is suitable for AI clusters that require higher bandwidth than 100G but are not yet ready for full 400G infrastructure. QSFP56 is commonly used for GPU-to-leaf connections and supports both InfiniBand HDR and 200GbE protocols .

Deployment Strategies

1. Backward Compatibility: QSFP-DD modules allow AI data centers to integrate legacy 100G/200G servers with next-generation 400G/800G networks, reducing upgrade costs and complexity .
2. Breakout Architectures: Using 800G OSFP ports with 2x400G QSFP112 breakout cables doubles server-to-switch connectivity without increasing rack footprint, improving thermal management and reducing cabling complexity .
3. Network Topology: Spine-leaf designs with QSFP-DD or QSFP112 ensure high-speed east-west communication between GPUs, which is critical for distributed AI training and large language model workloads .
4. Protocol Selection: RoCEv2 Ethernet and InfiniBand are both viable for AI clusters. RoCEv2 with QSFP-DD can deliver near-InfiniBand performance at lower cost, while InfiniBand may be preferred for ultra-low latency requirements .

Practical Considerations

• Thermal Management: High-speed transceivers generate significant heat; QSFP112 and QSFP-DD modules are designed for improved airflow and power efficiency .
• Scalability: QSFP-DD and QSFP112 support 400G and 800G networks, enabling future-proofing for AI clusters as GPU generations advance (e.g., H200, B200/B300 GPUs), .
• Bandwidth Planning: Each NVIDIA H100 GPU can push 400G traffic; proper QSFP deployment ensures full utilization of GPU compute resources without network bottlenecks .

Recommendation

For modern AI server deployments:

• Use QSFP-DD for high-density 400G/800G spine-leaf networks with backward compatibility.
• Deploy QSFP112 for flexible breakout connections to AI GPUs, optimizing rack density and thermal efficiency.
• Consider QSFP56 for intermediate 200G deployments where cost or infrastructure constraints prevent immediate 400G adoption. This combination ensures scalable, low-latency, and high-throughput connectivity for AI clusters, maximizing GPU utilization and supporting future network upgrades.