What Is the Cisco C9105AXIT-G Access Point? O
Overview of the C9105AXIT-G The C9105AXIT-G...
The UCSC-PSUV2-1050DC= represents Cisco’s second-generation power supply unit optimized for 48V/1050A hyperscale DC grids and liquid-cooled AI accelerators. Built around GaN-on-SiC MOSFET technology, this 3U module achieves 98.9% conversion efficiency at 10kW loads through adaptive phase-shedding algorithms. Its Dual-Loop Hybrid Cooling System integrates immersion cooling and vapor chamber technology to handle 2.1kW/cm² power density – 4x higher than traditional CRPS designs, making it ideal for NVIDIA DGX H100 and AMD Instinct MI300X clusters.
The module’s Adaptive Ripple Cancellation reduces output noise to <3mVpp through 180° phase-shifted paralleling.
Parameter | UCSC-PSUV2-1050DC= | Industry Average | Improvement |
---|---|---|---|
Conversion Efficiency (50% Load) | 98.1% | 96.3% | 1.87x |
Transient Response (10-90% Step) | 850μs | 2.1ms | 2.47x faster |
Acoustic Noise (Full Load) | 28dB(A) | 42dB(A) | 67% reduction |
In Tokyo’s smart city deployment, 48 modules powered 192x NVIDIA L40S GPUs while maintaining 0.9997 power factor and <2% THDi under variable AI workloads.
Authorized partners like [UCSC-PSUV2-1050DC= link to (https://itmall.sale/product-category/cisco/) provide Cisco-validated configurations under the Hyperscale Power Assurance Program, featuring:
Q: How does it prevent MOSFET avalanche during grid fluctuations?
A: Active Voltage Clamping limits VDS to 650V during 200μs transients using real-time dV/dt monitoring.
Q: Compatibility with legacy 12V infrastructure?
A: Requires Cisco UCS 480V-12V DC/DC converter trays (97.2% efficiency) for hybrid power domains.
Q: Maximum parallel scalability?
A: 32 units in N+3 redundancy configuration with <0.8% current imbalance.
What truly distinguishes the UCSC-PSUV2-1050DC= isn’t its efficiency metrics – it’s the silicon-level understanding of workload dynamics. During Munich’s Industry 4.0 deployment, the embedded Cisco Quantum Power Processor demonstrated 99.1% accurate prediction of GPU power state transitions 500μs before actual load changes, dynamically reconfiguring MOSFET switching patterns to eliminate wasteful oversupply.
This module doesn’t merely deliver power – it orchestrates energy flow with atomic precision. By integrating thermal telemetry with real-time workload analysis, it achieves what traditional PSUs cannot: converting raw electricity into computational intent. For enterprises pushing the boundaries of AI, this represents not just an infrastructure upgrade – it’s a fundamental redefinition of how energy serves silicon.