980-9IAU1-00XM00 | OSFP-1.6T-2DR4F | Transceiver
1.6T OSFP224 2xDR4 500m Transceiver with 2 exact SKU options. Confirm the host, port and firmware before quotation.
RoCE Ethernet Interconnects
Catalog reference: NVIDIA/Mellanox Compatible 1.6TBASE-2xDR4 OSFP IHS/Finned Top 8x200G PAM4 1310nm 500m SMF DOM Dual MPO-12/APC Linear-drive Receive Optics (LRO) Transceiver Module
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NVIDIA/Mellanox compatible 1.6T OSFP224 LRO transceiver eliminates receive-side DSP to reduce power and latency, consuming ≤17W while lowering cooling requirements and maintaining reliable signal transmission with seamless interoperability. This enables more efficient and scalable next-generation 1.6T network deployments. Ideal for AI clusters, HPC systems, and 1.6T networking.
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Review boundary: These identifiers reproduce source catalog matching terms only; they do not establish interoperability, approval or endorsement. Confirm the exact host model, port type, firmware and test evidence before use.
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NVIDIA/Mellanox Compatible 1.6T OSFP224 2xDR4 1310nm 500m SMF PAM4 LRO Optical Transceiver
NVIDIA/Mellanox compatible 1.6T OSFP224 LRO transceiver eliminates receive-side DSP to reduce power and latency, consuming ≤17W while lowering cooling requirements and maintaining reliable signal transmission with seamless interoperability. This enables more efficient and scalable next-generation 1.6T network deployments. Ideal for AI clusters, HPC systems, and 1.6T networking.
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Product brand: SUNFULL. Third-party names identify requested compatibility or coding targets only and do not imply affiliation, endorsement or original-equipment status.
Engineering record
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Technical detail
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Unlike conventional retimed optics with full DSP (Tx+Rx), LRO eliminates the receive-side DSP. This reduces module power consumption to ≤17W from 25W, saving over 8W per module to improve energy efficiency and lower cooling costs. The linear receive architecture shortens signal processing paths for lower latency, while the retimed transmit path ensures robust link compatibility across diverse host platforms. Optimized for next-generation AI clusters, HPC systems, and 1.6T networking, LRO provides an ideal balance of power, latency, and interoperability for large-scale AI fabrics.

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