G1508-1600WNA – 1600W CRPS Power Supply for Edge Datacenters, Real-Time Processing Systems, and Advanced Network Appliances
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): | 1600 |
| Length (mm): | 185 |
| Width (mm): | 73.5 |
| Height (mm): | 40 |
| Mounting Type: | Hot pluggable |
| Minimum Output Current (A): | 0 |
| Maximum Output Current (A): | 133 |
| Output Voltage (V): | 12 |
| Minimum Output Power (W): | 0 |
| Maximum Output Power (W): | 1600 |
| Minimum Input Voltage (V): | 90 |
| Maximum Input Voltage (V): | 264 |
Model Selection Comparison Table
|
Model |
Power Tier | Behavior | PMBus | Form Factor | Recommended Use |
| G1508-1600WNA | Mid-high performance | Compute/storage balanced | Yes | CRPS |
Virtualization + inference |
| Large enterprise compute | Heavy AI/HPC | Yes | CRPS | Multi-GPU, HPC fabrics | |
| G1505-2700WNA | Entry high-capacity | GPU/Compute dense workloads | Yes | CRPS |
HPC / inference clusters |
| Maximum performance | Heavy accelerated compute | Yes | CRPS |
AI + multi-node processing |
Deployment Scenarios
G1508-1600WNA is the higher-capacity performance model in the G1508 platform, targeting edge-scale compute, multi-NIC gateways, distributed storage blocks, virtualized clusters, and medium AI inference pipelines that demand stable rail delivery beyond 1200W-class envelopes. It is positioned for environments where workload concurrency and memory traffic generate continuous mid-to-high utilization, but thermal and cost efficiency still matter as deployment scale increases.
This watt tier provides comfortable operational margin for 2–4 accelerator cards, NVMe-rich storage appliances, mid-density inference racks, content delivery processors, and mixed virtualization nodes. Its rail stability under moderate step loads reduces jitter during container burst launches, storage index rebuilds, encryption tasks and micro-batch inference surges, helping preserve latency performance without premature redundant PSU scale-out.
|
Scenario |
Load Pattern | Why 1600W Applies |
| Multi-NIC gateway nodes | Mixed compute + I/O |
Sustains dual-accelerator duty |
|
NVMe storage blocks |
High write concurrency | Ripple remains controlled |
| VM/Container clusters | On-off burst cycles |
Transient suppression capacity |
|
Mid-range inference |
Batch + streaming | Rail remains stable near 80–90% |
| CDN/Edge processor | Continuous streaming |
Long duty thermal balance |
|
Database middleware |
Commit-heavy traffic |
Low waveform distortion risk |
Power Architecture & Reliability Design
The G1508-1600WNA balances conduction headroom with thermal efficiency, enabling long-life deployment in mixed storage–compute roles without prematurely entering high-power stress conditions. Its power path routing reduces conduction loss under steady transactional workloads, while the switching cadence and magnetic layout control temperature rise during mid-density inference streams, regional CDN cache serving, and container platform expansion cycles. Voltage rails remain stable through rapid utilization swings, limiting ripple propagation into latency-sensitive compute and networking fabrics.
Thermal design emphasizes even load distribution rather than peak dissipation. Heat is spread uniformly across switching elements and capacitor domains to delay electrolyte aging during continuous 24/7 operation. Ripple suppression remains consistent during NVMe burst writes, metadata rebuild events, and ingestion-heavy workloads, preventing secondary latency effects under parallel IO pressure. EMI distribution and spread-spectrum control further reduce signal interference when deployed in dense backplanes with high-speed NICs and transceivers.
PMBus telemetry provides operational transparency into voltage drift behavior, fan duty response, ripple vector evolution, and thermal slope variation, supporting predictable operation through real-world events such as firmware migrations, node scaling, and index rebuild traffic. Positioned between the 1200W and 2000W classes, the G1508-1600WNA is well suited for environments where compute and storage demand is substantial, but full 2000W provisioning is not yet required.
Power Operating Notes
|
Reference Condition |
Suggested Guidance |
| VM/Container workloads |
Maintain airflow for sustained >70% load |
|
Storage/NVMe clusters |
Monitor ripple after long write windows |
| Edge compute/Nodes |
Inspect thermal slope quarterly |
|
CDN/Streaming |
Track fan curve under peak nights |
| 24/7 operation |
Clean intake paths to protect efficiency |
|
Workload scaling |
Add redundancy when consistently >90% |
| Burst-heavy inference |
Monitor PMBus transient logs monthly |
|
Node future expansion |
Consider shift to 2000W when GPU count grows |
FAQ
Q1. When is G1508-1600WNA preferred?
When mixed compute/storage tasks run near-steady load and require overhead beyond 1200W tier.
Q2. Difference vs 2000W?
2000W suits heavier AI acceleration; 1600W fits balanced compute where efficiency matters.
Q3. PMBus availability?
Full telemetry for predictive lifecycle planning.
Q4. Suitable for 24/7 DC workloads?
Yes — thermal design targets always-on edge and datacenter conditions.
Q5. Use case precision?
Virtualization pools, NVMe nodes, mid-range inference clusters.
Q6. Thermal guidance?
Stable cooling keeps rail behavior linear under 80–90% sustained load.
Q7. Reliability positioning?
Optimized for long-term deployment where electrical stability > raw watt ceiling.
Q8. Scaling advice?
Upgrade to 2000W when workload expands into heavy multi-GPU territory.