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Product Overview

NADDOD 400G-1.6T modules come in OSFP, QSFP-DD, QSFP112 form factors, providing the most suitable multimode/singlemode solutions based on application scenarios. The product portfolio includes 1.6T OSFP DR8 SiPh modules(500m), 800G 2×SR4(50m), 2×DR4(500m), 2×FR4(2km), 2xXDR4(2km), 400G SR4(100m), VR4(50m), SR8(100m), DR4(100m), FR4(2km), XDR4(2km), LR4(10km), LR8(10km), ER8(40km), ER4(40km) for meeting different needs. NADDOD constructs a high-bandwidth, high-throughput, low-latency network for robustness, scalability, and cost-effectiveness in HPC and AI-driven data centers.

Related Blogs

NADDOD

OFC 2025 Recap: Key Innovations Driving Optical Networking Forward

Discover the highlights from OFC2025—large‑scale 800G/1.6T deployments to Silicon Photonics (SiPh), Linear Drive Pluggable Optics (LPO), and Co-Packaged Optics (CPO) innovations, novel fiber&connectivity advances, and chip‑level leaps driving the next generation of high‑bandwidth, energy‑efficient optical communications.
NADDOD

Broadcom Thor 2 vs NVIDIA CX7: 400G Ethernet NIC for AI/ML Workloads

Explore how Broadcom Thor 2 and NVIDIA CX7 400G Ethernet NICs compare in powering AI/ML workloads. This article examines their performance, power efficiency, reliability, and scalability to help optimize your high-performance networking infrastructure.
NADDOD

Why AI/ML Networks Rely on RDMA?

RDMA plays a vital role in AI/ML deployments by enabling faster data transfers, reducing latency, and minimizing CPU load. Its integration into InfiniBand and Ethernet networks supports efficient AI/ML infrastructures, powering applications like training, inference, and reinforcement learning while enhancing overall network performance.
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