Traditional Cooling is Static. CooledAI is Fluid.

Traditional systems use simple rules: if temp > 40°C, turn on fans. If temp < 35°C, turn them off. The result is a jagged, reactive response—bouncing, overshooting, and wasted energy. The system is always chasing the last spike, never anticipating the next one.

CooledAI uses inference-based logic. We don't wait for a sensor to spike. We predict thermal demand from workload scheduling, GPU voltage draw, and ambient conditions. The result is a smooth, predictive curve—no oscillation, no overshoot, no wasted cycles. Scales from single-rack pilots to multi-megawatt fleets.

Traditional Cooling
30°35°40°45°50°0m2m4m6m8m10mSetpoint 35–40°CTime (minutes)Temp (°C)SpikeSpike

Jagged, reactive. If temp > 40°C, turn on fans.

CooledAI
30°35°40°45°50°0m2m4m6m8m10mTarget 38°C ±1Time (minutes)Temp (°C)Predictive · No bounce

Smooth, predictive. Anticipate before the spike.

Trained on 500,000+ Thermal Failure Hours.

Our model isn't a generic AI. It's a specialized model trained on high-density server data. It understands how heat builds up in specific chips: NVIDIA H100s, AMD EPYC, and the next generation of AI accelerators.

The training data includes real failure scenarios—thermal runaways, cooling outages, workload spikes—from data centers running at the edge of capacity. The model learned to predict and prevent, not just react.

1Rack TelemetryTemp · Power · LoadAmbient · Room temp · Flow2Predictive EngineAI Model · Heat predictionThermal runaway · Time-to-failure500K+ thermal failure hoursNVIDIA H100 · AMD EPYC · BlackwellWorkload ScheduleJob schedule · Pre-cool triggers3Fan / ChillerControlPre-cool · Ramp · Throttle<1ms on-site

Knows Your Hardware

CooledAI knows the hardware. It understands the heat pattern of different AI workloads—training vs. inference, batch vs. real-time—and adjusts cooling before the workload even starts.

An H100 under full training has a different heat pattern than one running inference. Our model has learned these patterns. Pre-cooling kicks in when the job is scheduled, not when the chip starts to heat.

Zero-Latency Edge Deployment

Our optimization doesn't happen in a slow cloud. It runs as a lightweight edge agent—locally in the data center—for sub-millisecond safety response. No round-trip to a remote API. No network latency. No single point of failure.

When a thermal anomaly is detected, the agent responds in milliseconds. When a workload spike is predicted, pre-cooling ramps before the heat arrives. The edge deployment isn't just faster; it's the only architecture that can meet the real-time demands of high-density AI infrastructure. Scales from single sites to global multi-site deployments.

Experience the Shift in Heat Management.