We train transformer-based thermal forecasting and reinforcement-learning HVAC control models that reduce data center cooling energy by up to 40% — deployed from H100 training clusters to Jetson edge devices.
Our models combine physics-informed neural networks with sequence-to-sequence transformers. They learn the thermal dynamics of a data center — airflow, heat transfer, workload coupling — and predict temperature distributions across zones before they happen.
ThermalNet processes live telemetry from thousands of IT, CRAC, and environmental sensors through a physics-informed transformer encoder. Fourier-domain positional encoding captures the periodic nature of thermal cycles. The decoder generates multi-zone temperature forecasts with calibrated uncertainty bounds.
Each model is trained, validated, and deployed independently — then composed into a closed-loop control system. All models export to ONNX and run quantized on NVIDIA Jetson edge hardware.
| Model | Architecture | Parameters | Training | Accuracy |
|---|---|---|---|---|
|
ThermalNet
xkool-thermal-v2.1
Multi-zone thermal forecasting. Predicts temperature distributions across data center zones 2–6 hours ahead using a physics-informed transformer encoder with temporal decoder.
|
Transformer 12-layer encoder 8 heads, d=768 Fourier positional encoding |
34.2M F32 inference INT8 edge quant ONNX export |
3,200 GPU-hrs 8× H100 SXM batch=256 bf16 mixed precision |
MAE @ 2h horizon
|
|
CoolRL
xkool-control-v1.4
Reinforcement-learning HVAC controller. Optimizes airflow, CRAC setpoints, and valve positions in real-time per zone. Reward: energy cost subject to thermal SLA constraints.
|
PPO + GNN Actor-critic Graph neural net zone adjacency |
61.8M 2 policies action space=512 continuous control |
8,400 GPU-hrs 8× H100 SXM rollout parallelism 16 envs / GPU |
energy reduction vs baseline
|
|
WorkloadPredict
xkool-workload-v1.0
Forecasting model for workload-thermal coupling. Predicts GPU/CPU heat generation from job schedules, enabling pre-cooling of zones before high-compute jobs start.
|
Temporal Fusion multi-horizon attention + LSTM covariate mixing |
12.4M F32 inference INT8 edge quant quantile output |
1,100 GPU-hrs 4× H100 SXM quantile loss probabilistic |
P90 quantile coverage
|
Models are trained on NVIDIA H100 clusters, validated against digital twins in Omniverse, and deployed as quantized INT8 graphs to Jetson Orin edge devices running inside the data center. The full loop — sensor read, inference, control action — completes in under 200ms.
# ThermalNet v2.1 — distributed training on H100 cluster import torch from torch.distributed.fsdp import FullyShardedDataParallel as FSDP model = ThermalNet( d_model=768, n_heads=8, n_layers=12, sensor_channels=4096, physics_informed=True, uncertainty="mc_dropout", ) model = FSDP(model, cpu_offload_policy=None) optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=0.01) for epoch in range(100): for batch in dataloader: # 10M+ hrs sensor data loss = mse_loss(model(batch.sensors), batch.temps) loss += physics_regularization(model, batch) # heat eq. constraint loss.backward() optimizer.step() optimizer.zero_grad() if epoch % 10 == 0: export_onnx(model, quantize="int8") # Jetson edge deploy
Deployed across 3 partner data centers (totaling 42MW IT load). Results measured over 6-month controlled trials against standard PID-based cooling controllers. PUE measured per ASHRAE 90.4 methodology.
| Metric | Baseline (PID) | Xkool | Delta |
|---|---|---|---|
| PUE (facility-wide) | 1.55 | 1.15 | −25.8% |
| Cooling energy (kWh/rack) | 4,820 | 2,892 | −40.0% |
| Thermal SLA violations | 3.2 / week | 0.4 / week | −87.5% |
| Max inlet temperature | 32.4°C | 27.1°C | −5.3°C |
| Forecast MAE (2h) | 1.12°C | 0.28°C | −75.0% |
| Control loop latency | — | 200ms | real-time |
Xkool is an AI company focused on one problem: making data center cooling intelligent. We are a NVIDIA Inception member building the models, training infrastructure, and edge deployment stack for next-generation thermal management.
Data centers consume 1–1.5% of global electricity, and cooling accounts for up to 40% of that energy. As AI workloads scale exponentially, the thermal problem becomes a first-order constraint — not just on cost, but on compute density and reliability.
We are building the thermal intelligence layer: AI models that understand the physics of heat in a data center, predict thermal events before they occur, and control cooling infrastructure in real-time. Our models are trained on NVIDIA GPU clusters and deployed to edge devices running inside the facilities they optimize.
The result: 40% less cooling energy, PUE 1.15, and the ability to safely run higher-density racks without thermal risk — directly enabling the next generation of AI compute.