BUSIFLEX AI POWER REQUIREMENTS
AI server farms require massive amounts of power, especially when operating at scale. Here's a breakdown of their power needs and considerations:
⚡ AI POWER REQUIREMENTS
🔹 1. Power Usage Overview
- Small cluster (10–100 GPUs):
~20–200 kilowatts (kW) - Mid-size AI data center (1,000+ GPUs):
~1–5 megawatts (MW) - Large-scale supercomputing AI farms (e.g., GPT-4 training):
>100 MW or more - Example: Training GPT-3 was estimated to consume ~1,300 megawatt-hours (MWh) of electricity.
🔹 2. Power per Component
| Component | Power Usage (Approx.) |
|---|---|
| NVIDIA H100 GPU | ~700 watts (under load) |
| A100 GPU | ~400–500 watts |
| CPU (per node) | ~200–300 watts |
| Memory/RAM | ~50–100 watts/node |
| Cooling System | 30–50% of total IT power |
| Networking gear | ~5–10% of total power |
A single rack can draw 10–40 kW, depending on density and cooling setup.
🔹 3. Power Supply Infrastructure
- High-voltage feeds: 10kV–30kV lines coming into facility.
- Transformers & UPS: Convert and regulate voltage.
- Battery Backup (UPS) and diesel generators ensure uptime.
- Redundancy (N+1, 2N) for critical systems.
🔹 4. Efficiency Metrics
- PUE (Power Usage Effectiveness) = Total Facility Power / IT Equipment Power
- Ideal: 1.1–1.4
- Poor: 2.0+
Efficient AI farms aim for low PUE by optimizing cooling and power distribution.
🔹 5. Trends Toward Green AI
Partnering with solar, wind, hydro energy providers.
Building near dams or nuclear plants for sustainable supply.
Using liquid/immersion cooling to reduce HVAC energy.