G1508-2000WNA – 2000W CRPS Power Supply for AI Acceleration Platforms, Modular Server Nodes, and High-Bandwidth Network Systems
The G1508-2000WNA is a 2000W CRPS power module built for AI acceleration platforms, modular server nodes, and high-bandwidth networking systems that demand strong, stable, and high-efficiency power delivery. Designed in a compact 1U CRPS enclosure (185 × 73.5 × 40 mm), it integrates seamlessly into dense compute and switching architectures.It provides a powerful 12V main output rated at 166A along with a 12V standby rail at 2.1A, supplying clean and reliable power to GPUs, CPU clusters, switch fabrics, and control logic. The wide input range of 90–264Vac / 180–300Vdc ensures compatibility with global AC and DC infrastructures. With digital control and PMBus 1.2 support, the G1508-2000WNA enables precise telemetry, system-level diagnostics, and flexible power parameter adjustments—ideal for environments where performance tuning and predictive maintenance are essential. An adaptive cooling system manages fan speed intelligently to balance noise and thermal stability, while the optional reverse-airflow configuration expands possibilities for front-to-back or back-to-front cooling paths in complex enclosures.
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): | 2000 |
| Length (mm): | 185 |
| Width (mm): | 73.5 |
| Height (mm): | 40 |
| Mounting Type: | Hot pluggable |
| Minimum Output Current (A): | 0 |
| Maximum Output Current (A): | 166.6 |
| Output Voltage (V): | 12 |
| Minimum Output Power (W): | 0 |
| Maximum Output Power (W): | 2000 |
| 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
G1508-2000WNA sits at the top power tier within the G1508 series, engineered for HPC-class nodes, heavy virtualization clusters, distributed AI inference, micro-batch training workloads, and data pipelines with sustained high draw and frequent burst activity. Compared with the 1600W variant, this model offers greater continuous overhead for GPU-rich environments, dense NVMe expansion, and high concurrency edge aggregation, delaying the need for rack-level PSU scaling in fast-growing deployments.
The available watt envelope is suitable for 4–6 accelerator cards depending on thermal design, mixed CPU+GPU racks, NoSQL/columnar DB clusters, video encoding farms, and real-time analytics layers. Under continuous throughput compute, the PSU maintains waveform accuracy and thermal slope stability, which is crucial when AI tasks transition between inference and light fine-tuning or when storage indexing initiates under live load.
|
Scenario |
Load Pattern | Why 2000W Applies |
| Multi-GPU AI nodes | Sustained high draw + bursts |
Headroom for expansion + thermal margin |
|
Distributed inference |
Real-time pipeline | Stable rails under batch fluctuation |
| NVMe & object storage | Heavy write IO |
Bulk smoothing prevents jitter |
|
Virtualization clusters |
Long-duty utilization | Comfortable capacity at >80% load |
| Video encoding farms | Continuous compute |
Fan curve stays linear |
|
Data pipelines |
Mixed CPU/GPU |
PMBus aids transient visibility |
Power Architecture & Reliability Design
The G1508-2000WNA is designed to support compute-dense operation at sustained high utilization without destabilizing voltage curves, enabling predictable service uptime in GPU-centric clusters and HPC workloads. Its conduction path and magnetic layout are optimized for long-duty, high-current draw, maintaining rail stability during continuous encoding tasks, large inference batches, sharded database rebuilds, and storage compaction events where load persistence is the norm rather than the exception.
Electrical control emphasizes ripple moderation and harmonic dispersion to protect PCIe lanes and high-speed interconnect fabrics, particularly in configurations where multiple accelerators and NICs share power rails under real-time scheduling. Bulk capacitance and switching behavior work together to absorb burst peaks from synchronized kernel launches or parallel write flush cycles, reducing transient overshoot and minimizing the risk of micro-oscillation in dense system topologies.
PMBus telemetry provides detailed visibility into thermal slope deviation, ripple propagation trends, fan duty scaling, and component aging indicators, supporting lifecycle forecasting and preventive PSU rotation planning at data center scale. EMI behavior remains stable even in transceiver-dense or RF-noisy racks, while controlled startup inrush reduces stress on rack PDUs during mass boot events. Positioned where 1600W configurations begin to constrain growth, the G1508-2000WNA enables scaling toward 4–6 GPU platforms without immediate redundancy or power architecture expansion.
Power Operating Notes
|
Reference Condition |
Suggested Guidance |
| Continuous >80% load |
Maintain aggressive airflow path |
|
Multi-GPU nodes |
Track rail ripple under peak compute |
| DB/index workloads |
Monitor thermal curve at high IO |
|
AI inference/training mix |
Review PMBus transient logs |
| 24/7 heavy environment |
Clean intake pathways regularly |
|
Rack-wide nodes |
Stagger boot to reduce inrush |
| Planned scaling |
Expect power draw growth over lifecycle |
|
Expansion beyond design |
Move to redundant or higher capacity |
FAQ
Q1. Where does G1508-2000WNA fit?
In high-density compute workloads, particularly AI acceleration and HPC fabrics.
Q2. Difference vs 1600W?
Provides significantly more headroom for GPU counts, heavier concurrency and long-duty workloads.
Q3. PMBus support?
Yes — full telemetry for proactive planning and monitoring.
Q4. Reliability under 24/7?
Thermal distribution and switching design target continuous datacenter operation.
Q5. What workloads benefit most?
AI inference + training mix, encoding clusters, high-IO storage back ends.
Q6. How about startup behavior?
Controlled inrush reduces PDU load events in mass-boot conditions.
Q7. When to consider upgrade?
If workload expansion targets >6 GPUs or power envelope climbs to peak region often.
Q8. Can it anchor edge clusters?
Yes — where power density is high and lifecycle longevity is critical.