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Optical modules used in Huawei s supernode - GDR Telecom Site Energy Systems [PDF]
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In this case, how to use efficiently use meshes to interconnect the chips within large AI systems. Eyeing so-called SuperNodes – singular supercomputing clusters with upwards of a million
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CloudMatrix 384 super-node uses 6912 x 400G OSFP silicon photonic (SiPh) Linear Drive Pluggable Optics (LPO) optical modules and 3168 fibers, connecting 384 Ascend 910C
Large AI models involve both scale-up and scale-out networks, and AI computing supernode technology aims to integrate hundreds of GPUs/NPUs to form high-density computing
To do this, Huawei took a multifaceted strategy that includes a dual-chiplet HiSilicon Ascend 910C processor, optical interconnections, and the Huawei AI CloudMatrix 384 rack-scale
In the AI era, Huawei provides a full range of GE to 800GE optical modules, featuring three major capabilities: Spanning (ultra-long transmission), Stable (ultra-high reliability), and Secure (ultra-solid
As supernode penetration grows, demand should lift suppliers of optical modules, high-speed NICs, and liquid-cooling racks. Expect sustained investment in cluster engineering, software
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Huawei''s 910C expansion relies on a hybrid optical + electrical interconnect, whereas NVIDIA primarily uses electrical interconnects. As discussed earlier, the closer “1 + 1” can get to “2,”
Huawei announced the CloudMatrix 384 Supernode—a 16‑rack AI compute cluster achieving 300 petaflops of BF16 performance versus Nvidia''s 180 petaflops NVL72—using 384
Due to the extensive use of optical transceivers in both scale-up and scale-out network, this 384-GPU cluster is very power hungry. We estimate that one CM384 supernode draws close to
The Ascend 384 supernode uses an all-optical interconnect architecture (3,168 optical fibers + 6,912 400G optical modules), replacing traditional copper cables to achieve long-distance,
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