G1184-2200WNA–1200W CRPS Power Supply for AI Training Nodes, Cloud Storage, and Distributed Compute Systems
The G1184-2200WNA is a 1200W CRPS power supply designed for AI training infrastructure, cloud storage platforms, and distributed compute systems where power density and stability are mission-critical. Its 1U CRPS-standard footprint (195 × 73.5 × 40 mm) enables easy deployment in high-density racks.It delivers a steady 12V output at 183A, along with a 12V standby rail at 2.1A, providing dependable power for GPUs, CPUs, memory arrays, and system control logic. The wide input voltage range of 90–264Vac / 180–300Vdc ensures compatibility across global data centers.With digital power control and PMBus 1.2 support, the G1184-2200WNA allows advanced monitoring, telemetry feedback, and integration into intelligent power orchestration frameworks. Its high efficiency helps reduce both heat and operational energy costs.Adaptive fan-speed technology adjusts cooling performance based on thermal demand, minimizing acoustic noise while safeguarding system reliability. A reverse-airflow option is available for flexible deployment in varied rack cooling designs, including front-to-back or back-to-front airflow environments.
Features
CRPS-195: 195×73.5x40mm (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
AI Data Centers
Cloud Computing
Enterprise IT Systems
Medical Imaging Equipment
Approvals
UL/cUL
CB
TuV-Mark
CCC/CQC
FCC
CE
NOM
BIS
Specifications
| Output Power (W): | 2200 |
| Length (mm): | 195 |
| Width (mm): | 73.5 |
| Height (mm): | 40 |
| Mounting Type: | Hot pluggable |
| Minimum Output Current (A): | 0 |
| Maximum Output Current (A): | 183 |
| Output Voltage (V): | 12 |
| Minimum Output Power (W): | 0 |
| Maximum Output Power (W): | 2200 |
| Minimum Input Voltage (V): | 90 |
| Maximum Input Voltage (V): | 264 |
Model Selection Comparison Table
|
Model |
Power Tier | Operating Behavior | PMBus | Form Factor | Recommended Use |
| G1184-2000WNA | Performance mid-high | Reliable continuous compute ceiling | Yes | CRPS |
POP clusters / inference / storage |
| Performance high tier | Additional GPU + workload headroom | Yes | CRPS |
HPC edge / dense scaling |
Deployment Scenarios
Serving as the highest-capacity model within the G1184 platform, G1184-2200WNA is engineered for dense compute environments where workloads run continuously close to upper power envelopes, particularly when nodes integrate GPU acceleration, heavy I/O pipelines, or multi-tenant virtualization under long duty cycles. Compared to the 2000W tier, the 2200W model introduces additional headroom for inference latency workloads, storage rebuild surges, and high-connection concurrency, reducing risk of power throttling during periods of compute saturation.
This PSU is commonly selected for AI inference gateways, HPC edge clusters, data pipeline processing nodes, hybrid compute-plus-storage infrastructures, and container platforms running real-time analytics. In racks with limited airflow or seasonal temperature variance, the 2200W tier maintains ripple control while holding conversion efficiency on sustained high load segments. It is particularly valuable for scaling environments—able to support expansion without restructuring power architecture early in deployment.
|
Scenario |
Load Characteristic | Why 2200W Fits |
| GPU inference clusters | High constant current |
Prevents rail limit under bursts |
|
HPC edge compute |
Long high-duty workloads | Sustained efficiency margin |
| Multi-tenant clusters | VM density high |
Stable rail under concurrency |
|
Data streaming nodes |
Real-time processing | Ripple is maintained under I/O |
| Storage + rebuild tasks | Scrub-intensive |
Rail authority under peak |
|
Scaling infrastructure |
Future expansion |
Avoids premature PSU migration |
Power Architecture & Reliability Design
G1184-2200WNA expands the platform’s electrical headroom to support dense compute maps, ensuring voltage and ripple stability even when systems operate near saturation across extended workloads. Its LLC topology and synchronous rectifier stages are tuned for reduced switching loss across 70–95% loading, while high-conductivity bus paths sustain rail discipline under GPU inference spikes. Thermal channel design ensures exit airflow remains linear, reducing capacitor stress and slowing dielectric aging over multi-year service windows.
PMBus visibility enables gradual degradation detection—fan duty acceleration, temperature offset creep, ripple amplitude drift—so maintenance can be scheduled proactively based on telemetry rather than failure. In clustered N+1 environments, the PSU balances load predictably, minimizing imbalance heat and reducing cumulative bearing wear. For infrastructure operators planning multi-year rollout cycles, the 2200W tier offers resilient long-term power delivery for both compute growth and storage scaling, avoiding early PSU replacement when capacity shifts upward.
Power Operating Notes
|
Reference Condition |
Suggested Guidance |
| GPU-assisted workloads |
Maintain cold aisle intake for ripple headroom |
|
High-I/O cluster nodes |
Monitor PMBus thermal slope during scrub |
| Continuous >75% duty |
Track fan RPM curve to anticipate wear |
|
N+1 deployment |
Use matched series units for clean sharing |
| Streaming + inference |
Keep intake <35°C for stability span |
|
POP edge with dust risk |
Filter hygiene directly extends lifespan |
| Real-time data pipelines |
Telemetry logging recommended monthly |
|
Performance scaling |
2200W delays need for higher class PSU redesign |
FAQ
Q1. Best deployment targets for G1184-2200WNA?
HPC edge clusters, GPU inference workloads, high I/O POP compute, scaling virtualization, and real-time analytics.
Q2. Advantage vs 2000W?
More stable under dense workloads and better suited for GPU-heavy environments with prolonged utilization.
Q3. PMBus support?
Yes—complete telemetry for voltage, ripple, fan RPM, temperature, Lifecycle indicators.
Q4. Designed for 24/7?
Built for always-on clusters and constant compute service conditions.
Q5. GPU recommendation scope?
Ideal for medium-to-high inference duty depending on hardware and thermal integration.
Q6. Maintenance strategy?
Monitor fan duty curve & ripple drift to predict service timeline proactively.
Q7. Acoustic considerations?
Fans run higher under consistent load but maintain stable curve behavior.
Q8. Long-term upgrade path?
Only necessary when scaling toward multi-GPU or ultra-dense HPC racks above 2200W requirements.