\nOpen-source operating systems have their fundamental functional module code available as open-source. You can access the code from relevant communities and develop their features. Maintenance relies on yourself, making it more suitable for large-scale networking with their own development and maintenance teams, as it offers greater flexibility."}},{"@type":"Question","name":"What is the port density and speed of the N9500-128QC switch?","acceptedAnswer":{"@type":"Answer","text":"It supports up to 128*400GbE or 256*200GbE ports."}},{"@type":"Question","name":"What sort of modules and cables can be paired with the N9500-128QC switch?","acceptedAnswer":{"@type":"Answer","text":"It can use 400G optical modules in the QSFP112 package and cables, such as Q112-400G-SR4, Q112-400G-DR4, Q112-400G-CU*, etc."}}]}
Item Spotlights
N9500-128QC, 128-Port Ethernet L3 4U Data Center Switch, 128x 400Gb QSFP112, Preloaded with SONiC-based NOS, Support RoCEv2, Broadcom Chip
N9500-128QC is a leaf/spine switch for AI/ML Data Centers. It provides 128 QSFP112 400GE high-density ports and L2/L3 line-speed switching capabilities, and is backward compatible with 100GE/200GE interfaces. It supports smooth network expansion, and the basic Layer 2 network can support 8K 400G ports to calculate the network scale. The simplified network topology makes it easier to build a large-capacity, low-latency, non-blocking switching network, reduce the cost of network construction equipment and reduce energy consumption per Gbit, and further optimize and improve the performance of the entire network.
Optimized for high-density AI computing and next-gen data centers, the N9500-128QC provides 128x 400G ports in a compact 4U design, ensuring efficient, high-performance networking.
RDMA (Remote Direct Memory Access) technology based on Ethernet protocol enables low-latency, lossless communication, ensuring efficient data transfer and optimized AI workload performance.
Global load balancing, reactive path rebalancing, hardware link failover and drop congestion notification included in Cognitive Routing significantly enhance network capacity utilization and the job completion times for AI/ML training clusters.