G1179-1600WNA – 1600W 2U Power Distribution Board for Cloud Computing, Edge Data Centers, and Telecom Infrastructure
Features
2U Dimension: 265x77x84mm(LxWxH)
Input: 90 to 264Vac,180-300Vdc
Hot-plug
Full Digital control
Efficiency: Platinum/Titanium
Active Power Factor Correction
Reverse Airflow Option
Intelligent-thermal Fan Control
1+1 2U Redundant
Applications
Server
Storage
Networking
HPC
AI Centers
Cloud Platforms
Edge Computing
GPU Workstations
Approvals
UL/cUL
CB
TuV-Mark
CCC/CQC
FCC
CE
NOM
BIS
Specifications
| Output Power (W): | 1600 |
| Length (mm): | 265 |
| Width (mm): | 77 |
| Height (mm): | 84 |
| Mounting Type: | Hot pluggable |
| Output Current (V): | +12V/132A,+5V/25A,+3.3V/25A, -12V/0.5A,+5Vsb/3A |
| 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 Class | Use Role | Input | Form Factor | Recommended Use |
| G1179-0150WNA | Entry | Micro control compute | AC/DC | 1+1 2U PDB |
Monitoring & light routing |
| Mid | Edge + light VM | AC/DC | 1+1 2U PDB | Burst-capable deployment | |
| G1179-0350WNA | Upper-Mid | VM/storage light | AC/DC | 1+1 2U PDB |
Multiple services small scale |
| Mid-High | Multi-role compute | AC/DC | 1+1 2U PDB | Routing/IO concurrency | |
| G1179-0550WNA | High | Dense small servers | AC/DC | 1+1 2U PDB |
Heavier IO & VM |
| Strong | Storage + compute | AC/DC | 1+1 2U PDB | POP/CDN micro edge | |
| G1179-1300WNA | Extreme | Compute heavy nodes | AC/DC | 1+1 2U PDB |
GPU-lite workloads |
| Peak | AI/DB + compute | AC/DC | 1+1 2U PDB |
Scale-out fabrics |
Deployment Scenarios
The G1179-1600WNA represents the peak output tier of the G1179 family — designed for high concurrency compute clusters, SDS/object storage platforms, CDN/POP edge fabrics, multi-tenant cloud nodes, and GPU-assisted inference environments that run with continuous load intensity. This model supports scenarios where routing, storage IO bursts, VM scheduling, caching cycles, and inference tasks may occur simultaneously throughout long operational windows. With its 1600W power envelope, it provides robust surge tolerance and lifecycle headroom, minimizing risk of voltage compression during peak traffic hours.
|
Scenario |
Deployment Behavior | Why 1600W Tier Fits |
| Large edge clusters | Many concurrent VMs |
Stable under heavy concurrency |
|
SDS/object storage |
Frequent writes | Ripple resilience maintained |
| POP/CDN compute | Traffic-heavy |
Predictable burst handling |
|
GPU-lite inference |
Model streaming | Adequate surge power |
| Private cloud racks | Multi-role workloads |
Balanced & scalable |
|
Long-cycle 24/7 |
Continuous runtime |
High endurance margin |
Power Architecture & Reliability Design
The G1179-1600WNA is designed for scale-out compute fabrics where virtual machine density, SSD/NVMe I/O activity, routing traffic, and inference tasks aggressively occupy the available power envelope. Its power architecture is optimized to sustain high concurrency without instability, making it suitable for infrastructures that operate near peak utilization across extended service windows.
Voltage stability is maintained during multi-stream operations such as parallel VM execution, storage indexing, routing bursts, and GPU-lite inference workloads. Advanced ripple suppression protects storage subsystems from latency jitter during continuous I/O storms, helping preserve predictable performance when compute and data paths are simultaneously under pressure.
Thermal behavior is engineered to support non-stop duty cycles in edge racks and datacenter micro-zones where airflow margins may be limited. Heat distribution remains controlled during 24/7 operation, reducing long-term stress on components. PMBus insight provides operators with visibility into load patterns, thermal evolution, and lifecycle indicators, simplifying capacity monitoring, predictive maintenance, and replacement planning across large-scale fleet deployments.
Power Operating Notes
|
Reference Condition |
Suggested Guidance |
| High VM density clusters |
Plan airflow optimization to prevent localized thermal buildup under sustained VM load. |
|
SDS/object storage |
Monitor NVMe temperatures regularly during sustained read-write and rebuild activity. |
| POP/CDN nodes |
Maintain regular cleaning intervals to preserve airflow efficiency in edge environments. |
|
GPU-lite inference workloads |
VValidate the thermal operating window during burst inference and batch execution. |
| Hybrid cloud mix |
RReserve adequate surge margin to absorb workload transitions across mixed platforms. |
|
Long duty cycles |
Enable PMBus trend logging to observe gradual load and thermal drift over time. |
| IO spike workloads |
Ensure grounding quality to reduce electrical noise during rapid current changes. |
|
Future expansion |
Consider a multi-node scaling strategy rather than pushing single-node power density. |
FAQ
Q1. What workload profile is G1179-1600WNA ideal for?
Best for high-pressure multi-VM edge clouds, SDS storage grids, POP/CDN workloads, and moderate GPU inference, where power demand remains high for prolonged periods.
Q2. Can it operate under 24/7 continuous stress?
Yes. Its thermal and electrical characteristics are built for round-the-clock deployment, with stable voltage rails even under sustained IO and compute concurrency.
Q3. How does it compare to the 1300W tier?
The 1600W tier provides a larger safety envelope for heavy storage + routing concurrency, reducing risk of headroom exhaustion during peak load cycles or growth phases.
Q4. Redundancy support?
Fully supports 1+1 PDB architecture, enabling uninterrupted operation during PSU service or failure events in mission-critical installations.
Q5. Suitable for inference or GPU-lite acceleration?
Yes — appropriate for light inference workloads, or GPU-enhanced edge nodes where performance spikes occur unpredictably.
Q6. POP/CDN benefit?
Delivers reliable power for cache-heavy relay workloads, supporting frequent content turnover and routing bursts without ripple instability.
Q7. VM deployment expectation?
Supports dense VM clusters and multi-service parallelization, making it suitable for edge-cloud elasticity and scaling.
Q8. When to consider higher expansion?
If workloads evolve into AI model serving at scale, parallel GPU compute, or intensive SDS database clusters, deploy multiple nodes or transition into higher-platform series.