G1395-3500WNA –3500W CRPS Power Supply for POE Switches, Networking, Telecom, and AI Systems
The G1395-3500WNA is a high-performance CRPS power supply designed specifically for POE switches, networking equipment, telecommunications, and AI systems. Its compact 1U CRPS-standard form factor (185 x 73.5 x 40 mm) enables efficient use of space in dense system configurations.
This power supply delivers a main output of 64.22V at 55A, along with a 12V standby output rated at 3A. It supports a broad input voltage range of 90–264Vac and 180–300Vdc, making it suitable for global deployment.
Featuring full digital control and a PMBus 1.2 interface, the unit offers advanced power monitoring and remote management capabilities. Platinum-level efficiency combined with active power factor correction (PFC) ensures consistent, energy-efficient operation.
Smart fan control optimizes cooling performance while maintaining low noise levels. The reverse airflow option allows for flexible system integration to match various airflow designs.
Certified to meet UL, CE, FCC, CB, and other global safety and EMC standards, the G1395-3500WNA guarantees reliable and compliant performance in demanding environments.
Features
CRPS-185: 185×73.5x40mm(LxWxH)
CRPS-265: 265×73.5x40mm(LxWxH)
3A Max standby output current
Voltage Tolerance : 54.5Vdc ±3%(52.8-56.1Vdc)
Hot-plug
Full Digital control
Active Power Factor Correction
Intelligent-thermal Fan Control
N+1 Redundant
Reverse Airflow Option
Applications
POE Switch
Networking
Telecom
Artificial Intelligence
Router System
AI Data Centers
5G Base Stations
IoT Gateways
Approvals
UL/CUL
CB
TUV/Mark
CCC/CQC
FCC
CE
Specifications
| Output Power (W): | 3500 |
| Length (mm): | 185 |
| Width (mm): | 73.5 |
| Height (mm): | 40 |
| Mounting Type: | Hot pluggable |
| Minimum Output Current (A): | 0 |
| Maximum Output Current (A): | 64.22 |
| Output Voltage (V): | 54.5 |
| Minimum Output Power (W): | 0 |
| Maximum Output Power (W): | 3500 |
| Minimum Input Voltage (V): | 90 |
| Maximum Input Voltage (V): | 264 |
Model Selection Comparison Table
|
Model |
Power Class | Role | Input | Form Factor | Recommended Use |
| G1395-3000WNA | High-Density | Compute + storage blend | AC/DC | CRPS-185 |
Hybrid clusters and analytics |
| Extreme | GPU inference and POP compute | AC/DC | CRPS-185 |
Large AI and data bursts |
Deployment Scenarios
The G1395-3500WNA is the upper-tier power option of the G1395 lineup, delivering 3500W continuous output capacity for power-intensive systems that run heavy data pipelines, GPU inference blocks, or database workloads at scale. This model is ideal for HPC clusters, distributed vector storage, accelerated AI frameworks, LLM inference nodes, POP data aggregation, and 24/7 compute farms where the power envelope often resides near the edge of stability. Its larger headroom compared with 3000W ensures stable operation under combined GPU bursts, rebuild traffic surges, continuous indexing, and simultaneous multi-tenant requests, making it suitable for organizations designing forward-scalable compute infrastructure without recurring PSU upgrades.
|
Scenario |
Expected Load Pattern | Why 3500W Tier Fits |
| LLM inference clusters | High peak GPU draw |
Prevents droop during model execution bursts |
|
HPC scientific nodes |
Constant heavy compute | Sustains regulated output under 24/7 stress |
| High-density AI servers | Parallel GPU acceleration |
Extra margin reduces throttling events |
|
Multi-petabyte storage |
RAID rebuild + IO surge | Absorbs peak draw without brownout |
| Edge cloud POP | Mixed user traffic |
Stable under multi-stream container load |
|
Realtime vision analytics |
Event-driven activity |
Maintains stable rail during surges |
Power Architecture & Reliability Design
The G1395-3500WNA employs a reinforced AC-to-DC conversion stage optimized for environments dominated by persistent GPU acceleration and multi-container compute flows, where power draw rarely returns to idle. Its rail regulation is engineered to remain stable during sudden model load shifts, NVMe synchronization bursts, and distributed compute failover events, preventing voltage collapse as workloads rebalance under pressure.
Thermal architecture focuses on maintaining predictable cooling behavior in deep-rack deployments where static pressure is high and airflow margins are narrow. Internal routing and component placement are designed to sustain orderly heat dissipation even as ambient temperatures fluctuate, while high-endurance component selection supports low ripple operation and long service life under repetitive high-temperature duty cycles.
In redundant N+1 or N+N configurations, the power stage delivers consistent parallel behavior, enabling live migration and unit replacement without service interruption. Telemetry exposure provides clear visibility into load trends, thermal drift, and fan duty evolution, supporting predictive maintenance strategies rather than reactive intervention. As a higher-tier option within the G1395 family, the G1395-3500WNA provides additional watt margin to reduce compute throttling and sustain stable operation in high-density, always-on data center environments.
Power Operating Notes
|
Reference Condition |
Suggested Guidance |
| GPU-first acceleration |
Leave safety buffer for burst overhead |
|
HPC long-running jobs |
Monitor temp duty over multi-day loads |
| NVMe+Cache fabrics |
Expect stable rebuild voltage response |
|
AI inference/LLM serving |
Fan zoning improves thermal stability |
| High ambient environments |
Dust control improves performance over time |
|
Metro POP aggregation |
Telemetry polling enhances uptime planning |
| Cluster deployment |
Cable sizing to match continuous draw |
|
Global fleets |
Standardize series for simplified replacement |
FAQ
Q1. Where is this PSU best deployed?
High-density AI compute, GPU-accelerated clusters, HPC workloads, POP data units.
Q2. Can it run under 24/7 full utilization?
Yes — heat handling and regulation support continuous load operation.
Q3. Difference from 3000W version?
3500W maximizes headroom for sustained GPU workloads and rebuild spikes.
Q4. Does it support redundancy?
Yes — ideal for N+1 / N+N server configurations.
Q5. Does it suit multi-GPU inference nodes?
Yes, the watt margin reduces throttling in peak acceleration phases.
Q6. Suitable for large cloud inference pools?
Stable under long-tail concurrency and real-time scaling.
Q7. Deployment caution?
Maintain airflow clearance; deep racks may require higher static pressure mitigation.
Q8. When to choose 3500W over 3000W?
When AI acceleration demand is persistent and growth headroom is mandatory.