G1666-2700WNA – 2700W CRPS Power Supply for Cloud Data Fabrics, Edge GPU Clusters, and Telecom Core Equipment

The G1666-2700WNA is a 2700W CRPS power module engineered for cloud data fabrics, edge GPU clusters, and high-reliability telecom core equipment. Its 1U CRPS-standard form factor (185 × 73.5 × 40 mm) allows seamless deployment in dense compute and switching environments where footprint efficiency is essential. Delivering a robust 12V output rated at 225A alongside a 12V standby rail of 2.1A, it supports heavy parallel workloads, distributed AI inference nodes, and power-intensive control boards. The wide operating input range of 90–264Vac / 180–300Vdc ensures compatibility across global AC and DC infrastructures. With full digital control and PMBus 1.2, the G1666-2700WNA enables accurate telemetry, real-time diagnostics, and flexible system-level power adjustments. This is particularly beneficial for deployments where operational visibility and predictive maintenance reduce downtime risk. The cooling system incorporates intelligent fan modulation to maintain stable thermal behavior under fluctuating loads, and the optional reverse-airflow variant supports diverse rack airflow configurations, making integration easier across different hardware architectures.

Features

CRPS-185: 185×73.5x40mm (LxWxH)

CRPS-265: 265×73.5x40mm (LxWxH)

Input: 90 to 264Vac,180-300VdC

Hot-plug

Full Digital control

Active Power Factor Correction

Intelligent-thermal Fan Control

N+N N+1 Redundant

Reverse Airflow Option

Applications

Server

Storage

Networking

HPC

Edge Computing

Telecom

AI Training

Industrial Automation

Approvals

UL/cUL

CB

TuV-Mark

CCC/CQC

FCC

CE

NOM

BIS

Specifications

Output Power (W): 2700
Length (mm): 185
Width (mm): 73.5
Height (mm): 40
Mounting Type: Hot pluggable
Minimum Output Current (A): 0
Maximum Output Current (A): 225
Output Voltage (V): 12
Minimum Output Power (W): 0
Maximum Output Power (W): 2700
Minimum Input Voltage (V): 90
Maximum Input Voltage (V): 264

Model Selection Comparison Table

Model

Power Tier Behavior PMBus Form Factor Recommended Use
G1666-2700WNA High performance entry GPU/AI balanced workloads Yes CRPS

AI inference clusters

G1666-3200WNA

Higher density Multi-GPU heavy compute Yes CRPS HPC/LLM intermediate
G1666-3600WNA Peak class Dense training + large cluster Yes CRPS

High-end AI/HPC fabrics

G1667-4200WNA

Extended headroom Enterprise AI/LLM Yes CRPS Training + scaling
G1667-5200WNA Ultra capacity Maximum compute envelope Yes CRPS

Large-scale cores

Deployment Scenarios

G1666-2700WNA occupies the entry tier of the G1666 high-power family, serving as a balanced starting point for nodes stepping beyond the 2000W class into denser AI inference, mini-training clusters, multi-NIC topologies, PCIe Gen4/Gen5 accelerators, NVMe-heavy storage arrays, and mid-scale virtualization stacks. Its capacity enables 3–5 GPU mixed compute, often seen in enterprise LLM edge inference, cloud micro-training, encoding farms, CAD/CAE workstations, and fast-growth SaaS clusters where the system must retain power elasticity for component upgrades without early PSU replacement.

 

Voltage behavior remains stable even under load transitions — particularly relevant when inference batches spike or when high-speed NVMe scratch writes trigger brief power surges. Deployments running object storage layers, cache rebuild, heat-map generation, or multi-tenant Kubernetes clusters benefit from the overhead that prevents rail sag during peak density.

 

Scenario

Load Pattern Why 2700W Applies
Multi-GPU inference Steady + periodic burst

Power headroom reduces rail droop

Edge micro-training

Long duty utilization Sustains continuous AI workloads
NVMe storage tier Burst write spikes

Ripple stays within stable envelope

HPC/CAE nodes

Heavy FP/INT compute Maintains waveform smoothness
Virtualization farms Multi-tenant

Good overhead during migration load

Encoding/streaming

24/7 run

Thermal slope remains predictable

Power Architecture & Reliability Design

The G1666-2700WNA marks the first step into the high-power envelope where rail integrity under sustained GPU draw becomes a primary engineering objective. Its conduction path, magnetic structure, and switching geometry are optimized for long-duty operation in environments where compute load rarely drops below mid-range utilization, such as inference clusters and continuous model execution platforms. Voltage stability is maintained even as parallel execution creates burst-transient demand across accelerator cards and PCIe fabrics.

 

Thermal architecture emphasizes controlled airflow zoning to improve exhaust efficiency and delay hotspot saturation during prolonged operation. The layout of MOSFETs and inductors minimizes micro-oscillation under rapid load transitions, which is especially critical in GPU-dense systems where simultaneous kernel launches and data movement can stress power rails. Bulk capacitance further dampens I/O-driven burst spikes, helping storage and interconnect layers maintain consistency at high queue depth.

 

PMBus telemetry provides operators with visibility into ripple evolution, fan duty behavior, capacitor aging trends, and thermal deviation over multi-month cycles, enabling predictive maintenance strategies aligned with no-downtime SLA requirements. EMI propagation is shaped to preserve signal integrity in NIC-dense racks and accelerator pools where RF and switching noise are more pronounced. Positioned as the first scalable jump beyond 2000W, the G1666-2700WNA supports cluster growth scenarios that anticipate GPU expansion without immediate transition to extreme-watt or multi-rail power architectures.

Power Operating Notes

Reference Condition

Suggested Guidance
Sustained >70-85% load

Maintain stable exhaust airflow path

Multi-GPU inference farm

Track ripple at burst intervals
NVMe cluster writes

Monitor temperature under peak queue

Virtualized stack

PMBus transient log review recommended
24/7 duty deployment

Regular dust clearance + airflow audit

Rack-dense servers

Boot staggering prevents peak inrush
Scaling trajectory

Reserve margin for GPU expansion

High ambient DC

Verify fan curve stability at upper temp

FAQ

Q1. Who should deploy G1666-2700WNA?
Teams scaling into multi-GPU inference, NVMe storage acceleration, or HPC edge centers.

 

Q2. Compared with 2000W class?
Provides more breathing room for power bursts and reduces early system upgrade pressure.

 

Q3. PMBus support?
Yes — full telemetry for temperature, voltage, ripple, and fan duty.

 

Q4. Suitable for 24/7 datacenter duty?
Thermal spread and switching stage are tuned for long operational stability.

 

Q5. How about GPU count?
Ideal for 3–5 accelerator configurations, depending on node thermal design.

 

Q6. Recommended inrush practice?
Prefer staggered boot in rack-wide startup events.

 

Q7. When to move higher?
If workloads approach constant near-max utilization or GPU count increases, transition to 3200W/3600W.

 

Q8. Does it support predictive maintenance?
Yes — PMBus data history aids replacement planning and prevents failure-driven downtime.

 

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