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