G1667-5200WNA – 5200W CRPS Power Supply for GPU-Accelerated Servers, Hyperscale Data Centers, and AI Training Clusters
The G1667-5200WNA is a high-capacity 5200W CRPS power module built for GPU-accelerated servers, hyperscale data centers, and AI training clusters that demand extreme power density and continuous stability. Its 1U CRPS-standard form factor (185 × 73.5 × 40 mm) enables efficient deployment in high-density compute environments where rack space is at a premium. Delivering a primary 12V output at 433A along with a 12V standby output rated at 2.1A, this unit supports multi-GPU systems, large CPU arrays, and mixed-architecture compute nodes. The wide input range (90–264Vac / 180–300Vdc) ensures strong compatibility with global AC grids and DC power distribution commonly used in modern cloud facilities. Digital control combined with PMBus 1.2 provides detailed telemetry, fault reporting, and fine-grained power supervision—enhancing system reliability under dynamic AI and HPC workloads. A smart cooling mechanism optimizes thermal behavior through adaptive fan algorithms, maintaining stable operation even in tightly packed racks. Optional reverse airflow increases installation flexibility across varied thermal layouts.
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): | 5200 |
| Length (mm): | 185 |
| Width (mm): | 73.5 |
| Height (mm): | 40 |
| Mounting Type: | Hot pluggable |
| Minimum Output Current (A): | 0 |
| Maximum Output Current (A): | 433 |
| Output Voltage (V): | 12 |
| Minimum Output Power (W): | 0 |
| Maximum Output Power (W): | 5200 |
| Minimum Input Voltage (V): | 90 |
| Maximum Input Voltage (V): | 264 |
Model Selection Comparison Table
|
Model |
Power Tier | Behavior | PMBus | Form Factor | Recommended Use |
| G1667-4200WNA | Extended headroom | Enterprise AI/LLM | Yes | CRPS |
Training + scaling |
| Ultra capacity | Maximum compute envelope | Yes | CRPS | Large-scale cores | |
| 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 |
Deployment Scenarios
G1667-5200WNA stands at the top of the G1667 high-density PSU family, built for large-scale AI training clusters, multi-node LLM workloads, foundation model batching, vector search fabrics, hybrid inference pools and HPC supercomputing environments.
Compared to the 4200W tier, the 5200W model provides significant reserve capacity for 10–16 GPU compute nodes, high-bandwidth PCIe fabrics, large HPC memory domains and NVMe-accelerated data lakes. This level of power eliminates early PSU stacking and supports scale-up architectures where training cycles, optimizer passes and distributed gradient sync occur continuously.
It is especially effective where model fine-tuning, MoE workloads, retrieval-augmented inference, streaming ETL pipelines and multi-modal execution (Vision + Text + Audio) converge into high power demand patterns. High headroom prevents rail droop during checkpoint merges, dataset replay, high-frequency backprop intervals and GPU burst synchronization.
|
Scenario |
Load Pattern | Why 5200W Applies |
| Foundation/LLM training | Sustained near-peak |
Power stability through optimizer sweeps |
|
10–16 GPU compute racks |
Dense acceleration | Eliminates PSU bottlenecks |
| MoE / RAG pipelines | Irregular bursts |
Ripple remains controlled during spikes |
|
HPC simulation |
Heavy FP compute | Magnetic design ensures waveform discipline |
| Large NVMe pools | High IO write |
Bulk caps prevent transient jitter |
|
Central inference hubs |
24/7 cycles |
Thermal slope remains predictable |
Power Architecture & Reliability Design
The G1667-5200WNA is engineered for environments where the power ceiling directly defines cluster throughput, supporting ultra-dense AI deployments, large-batch training, and distributed HPC workloads that demand unwavering rail integrity. Its architecture is designed to sustain extreme electrical stress as synchronized GPU execution, optimizer aggregation, and long training windows push utilization deep into high-duty territory.
The switching stage incorporates advanced transient suppression to absorb simultaneous GPU kernel peaks during FP16 and FP8 execution, while staged capacitance preserves ripple discipline under multi-shard checkpoint writes and gradient aggregation events. This topology is optimized for continuous operation beyond the 80–90% duty range, reducing thermal fatigue and electrical drift that commonly limit stability in long-haul training environments.
Thermal design emphasizes controlled airflow channeling and broad heat spreading to delay fan curve escalation, maintaining efficiency in high-ambient cold aisles, sealed rack pods, and 24/7 parallel compute nodes. PMBus telemetry provides granular visibility into power behavior, ripple growth trends, thermal accumulation patterns, inductor saturation signals, and component aging metrics, enabling predictive lifecycle planning at hyperscale. Positioned for training clusters where no PSU headroom compromise is acceptable, the G1667-5200WNA delivers sustained performance without forcing immediate multi-PSU or distributed power redesign.
Power Operating Notes
|
Reference Condition |
Suggested Guidance |
| 80–95% utilization |
Confirm airflow and thermal path headroom |
|
10–16 GPU nodes |
Track PMBus ripple during sync phases |
| Multi-modal pipelines |
Review waveform logs under burst load |
|
NVMe data ingestion |
Monitor temperature plateau during write |
| Dense compute rooms |
Use staggered startup sequence |
|
High ambient racks |
Keep fan intake unobstructed |
| Future growth stage |
Provides capacity for GPU/domain scaling |
|
Hyperscale runtime |
Apply predictive replacement scheduling |
FAQ
Q1. When to select G1667-5200WNA?
When workloads require extreme GPU density, long-span training, and minimal PSU constraint.
Q2. Difference compared to 4200W?
Substantially more continuous load headroom for training-centric tasks + better transient absorption.
Q3. PMBus?
Yes — full telemetry for operational monitoring and maintenance planning.
Q4. Designed for 24/7 HPC?
Thermal and switching architecture support non-stop training operations.
Q5. GPU count guideline?
Typically 10–16 GPUs, depending on cooling and accelerator power profile.
Q6. Startup practice?
Staggered boot recommended for large parallel deployments.
Q7. When is this model insufficient?
Only when workloads exceed ultra-dense compute ceilings — otherwise this tier is rarely limiting.
Q8. Recommended deployment profile?
Large-model training clusters, multi-node compute mesh, hyperscale inference hubs.