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