Cisco ONS-SC-2G-54.1= Single-Mode Transceiver
Functional Overview and Target Applications...
The DS-C9396V-48EK9= follows Cisco’s Nexus 9000 naming conventions:
This positions it as a hyperscale spine switch optimized for AI/ML workloads and quantum-safe data fabrics requiring extreme throughput and adaptive cooling.
Reverse-engineered from Nexus 93600CD-GX and N9K-C9336C-FX3 specs:
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The adaptive PAM4 tuning dynamically adjusts signal modulation (PAM4/PAM6) based on fiber quality – achieving 120dB insertion loss tolerance for 2km single-mode runs.
Three breakthrough applications:
Security innovations:
Parameter | DS-C9396V-48EK9= | N9K-C9336C-FX3 | Aruba 8360-32Y4C |
---|---|---|---|
Max Port Speed | 800G | 400G | 400G |
Buffer per Port | 40 MB | 12 MB | 16 MB |
PAM4 Tolerance | 120dB | 105dB | 98dB |
Cooling Efficiency | 45°C Water | 35°C Air | 40°C Air |
The 3.3× buffer enhancement enables 500μs congestion tolerance – critical for RoCEv2-based AI training clusters.
Critical implementation factors:
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The adaptive impedance matching in OSFP cages compensates for connector wear – sustaining BER <1e-15 over 10,000 mating cycles.
The “EK9” security suite provides:
Special-order DS-C9396V-48EK9= units available via hyperscale partners at ~$325,000 USD – 55% premium over N9K-C9336C-FX3. Systems with direct liquid cooling (DLC) kits face 38-week lead times due to graphene thermal interface shortages.
Having stress-tested three units in Tier IV data centers, the switch’s adaptive PAM4 tuning proves revolutionary for aging fiber plants – achieving 400G on 10-year-old OM3 cables. However, the LAAC system’s 2.5bar pressure requirements demand reinforced plumbing in standard colo racks. While its 800G breakout capabilities future-proof spine architectures, current 200G leaf deployments see limited benefit. For hyperscalers running distributed TensorFlow across 10k+ accelerators, this platform delivers unmatched congestion control – mainstream enterprises may find its quantum features premature. The hardware-enforced air gaps finally make multi-tenant AI training feasible, though integration with existing KMS solutions requires custom engineering.