G1667-4200WNA – 4200W CRPS Power Supply for AI Infrastructure, Modular Data Centers, and High-Density Compute Nodes

The G1667-4200WNA is a 4200W CRPS power module engineered for next-generation AI infrastructure, modular data centers, and high-density compute nodes that demand exceptionally stable and scalable power delivery. Its compact CRPS-standard 1U form factor (185 × 73.5 × 40 mm) enables efficient deployment across dense server and accelerator platforms. Delivering a powerful 12V main output at 350A alongside a 12V standby output rated at 2.1A, this unit supports heavy AI accelerator arrays, multi-node compute clusters, and complex network fabrics. Its wide input compatibility (90–264Vac / 180–300Vdc) ensures smooth operation across global AC and DC power systems, including hybrid grid environments. Digital control architecture combined with PMBus 1.2 provides precise telemetry, real-time system supervision, and remote operational tuning—ideal for data centers optimizing energy profiles or managing distributed AI processing layers. Thermal stability is ensured with intelligent fan control that dynamically adjusts airflow based on load and temperature, while the optional reverse-airflow configuration supports flexible rack and chassis designs.

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): 4200
Length (mm): 185
Width (mm): 73.5
Height (mm): 40
Mounting Type: Hot pluggable
Minimum Output Current (A): 0
Maximum Output Current (A): 350
Output Voltage (V): 12
Minimum Output Power (W): 0
Maximum Output Power (W): 4200
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

G1667-5200WNA

Ultra capacity Maximum compute envelope Yes CRPS Large-scale cores
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

Deployment Scenarios

G1667-4200WNA marks the enterprise-grade training layer above the G1666 family, designed for mid-to-large AI compute clusters, distributed LLM training, vector DB acceleration, multimodal inference pipelines, and storage-heavy training nodes requiring continuous high-load stability.
With a substantial power envelope beyond 3600W, this PSU supports 8–12 GPU compute racks, high-bandwidth PCIe fabrics, and NVMe-based cache layers without early voltage fatigue, ensuring rail consistency even during gradient sync, large-batch inference bursts, dataset shuffling or model checkpoint routines.

 

Its headroom makes it practical for scaling inference pods toward training clusters without architectural redesign. In dense HPC deployments, the 4200W tier reduces frequency of PSU parallelization, easing PDU pressure and simplifying capacity planning for environments that run 24/7 long-job workloads with unpredictable demand spikes.

 

Scenario

Load Pattern Why 4200W Applies
Distributed LLM training High sustained power + bursts

Handles heavy parallel kernel draws

8–12 GPU compute racks

Dense acceleration Prevents rail droop under concurrency
Vector DB retrieval IO + memory heavy

Smooths ripple during query storms

Multimodal inference

Spiky utilization Magnetic design absorbs load shifts
Real-time render pipelines Long cycle workloads

Thermal drift stays under control

Scale-up clusters

Growth expected

Power margin delays PSU stacking

 

Power Architecture & Reliability Design

The G1667-4200WNA operates deep within the high-power compute zone, where rail behavior, transient recovery, and thermal stability directly determine cluster uptime. It is engineered for environments that routinely exceed inference-only workloads, sustaining voltage discipline during synchronized GPU execution waves driven by optimizer steps, dense forward and backward passes, embedding table access, and multi-shard checkpoint operations common in large-scale training pipelines.

 

Its switching topology and magnetic balance are refined to absorb instantaneous current draw when multiple accelerators peak simultaneously, flattening ripple propagation across heavy FP16 and INT8 execution. Capacitor distribution and transient handling work together to preserve waveform integrity during optimizer synchronization and gradient update phases, preventing latency jitter that can otherwise cascade through tightly coupled training fabrics.

 

Thermal zoning, MOSFET placement, and airflow alignment mitigate choke points during continuous operation in the 75–85% duty range, delaying aggressive fan escalation and maintaining predictable acoustic and thermal behavior over extended sessions. PMBus telemetry exposes fan curve evolution, ripple variance, and component aging trends, enabling predictive replacement cycles rather than reactive downtime. EMI discipline further stabilizes high-speed link integrity in racks dense with NICs, accelerators, and NVMe arrays. Positioned as a strategic transition step before ultra-high-capacity power architectures, the G1667-4200WNA supports training-class clusters without immediate PSU doubling or distributed power redesign.

Power Operating Notes

Reference Condition

Suggested Guidance
Near continuous training

Maintain clean front-to-back airflow

8–12 GPU nodes

Monitor ripple stability on PMBus
Vector DB indexing

Observe thermal ramp patterns

Dense compute pools

Use staggered cold boot
High ambient racks

Validate fan curve thresholds

NVMe cache bursts

Track temp during long runs
Future scaling

Allocate watt margin for upgrades

24/7 enterprise workloads

Use telemetry for predictive service

FAQ

Q1. Who should choose G1667-4200WNA?
Teams running enterprise training nodes, distributed inference and HPC-scale pipelines.

 

Q2. Key advance vs 3600W?
Higher power overhead for training concurrency, cache intensity & multi-GPU scaling.

 

Q3. PMBus support?
Yes — ideal for proactive thermal and power analytics.

 

Q4. Suitable for sustained training workloads?
Engineered for multi-hour compute sessions and stable voltage slopes.

 

Q5. GPU count guideline?
Typically 8–12 GPUs depending on rack cooling and board efficiency.

 

Q6. Startup procedure?
Prefer staggered activation in rack boot sequencing.

 

Q7. When to move up to 5200W?
When power utilization nears ceiling or GPU/fabric density doubles.

 

Q8. Can it support multimodal AI?
Yes — stable under vision+text inference, multi-domain execution and memory-heavy operations.

 

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