An enterprise-grade, real-time GenAI & Agentic AI Thermal Optimization Platform engineered specifically for the NVIDIA H100 SXM5 80GB (700W TDP) GPU architecture.
By replacing reactive, hardcoded BIOS fan curves with a 5-Stage Closed-Loop Agentic AI Controller (Observe → Reason → Plan → Act → Verify), a PyTorch Multi-Step LSTM Predictor, and a Vector Retrieval-Augmented Generation (RAG) Engine, this system proactively mitigates thermal throttling, prevents junction thermal runaway, reduces energy consumption, and extends hardware lifespan.
┌──────────────────────────┐
│ H100 GPU │
│ │
│ Utilization │
│ Power │
│ Temperature │
│ SM / Tensor utilization │
│ Memory / HBM metrics │
│ Clock │
└────────────┬─────────────┘
│
▼
┌──────────────────────────┐
│ Telemetry Collector │
│ nvidia-smi / DCGM │
└────────────┬─────────────┘
│
▼
┌──────────────────────────┐
│ Thermal State │
│ │
│ Current temp │
│ Temp velocity │
│ Power trend │
│ Workload trend │
│ Predicted temperature │
└────────────┬─────────────┘
│
▼
┌──────────────────────────┐
│ Thermal Predictor │
│ LSTM │
│ │
│ Predict T+1 ... T+N │
└────────────┬─────────────┘
│
▼
┌─────────────────────────┐
│ AGENTIC AI │
│ Thermal Controller │
│ │
│ Observe → Reason → Plan │
│ → Act → Verify │
└────────────┬────────────┘
│
┌────────────────┼────────────────┐
▼ ▼ ▼
Increase fan Reduce power Maintain
cooling limit settings
│ │ │
└────────────────┼────────────────┘
▼
H100 GPU
The system ingests and optimizes 10 core NVIDIA H100 parameters:
| Parameter | Effect on Temperature | Role in System |
|---|---|---|
| GPU Utilization (%) | ↑ utilization → ↑ compute activity → ↑ power → ↑ temp | Primary Input |
| GPU Power Usage (W) | ↑ power → ↑ heat generation | Primary Input / Target |
| GPU Clock / SM Clock (MHz) | ↑ clock generally → ↑ power/heat | Important Input / Advisory |
| Memory Clock (MHz) | ↑ HBM3 memory activity/clock → ↑ power | Important Input |
| GPU Memory Utilization (%) | HBM3 memory traffic power contribution | Secondary Input |
| SM / Tensor Core Utilization (%) | Heavy FP8/FP16 AI matrix workloads raise power significantly | Key H100 Workload Indicator |
| Fan Speed / Cooling Level (%) | ↑ cooling → ↓ temperature | Control Variable (Action 1) |
| Ambient / Inlet Temp (°C) | ↑ ambient → ↑ GPU temperature | Environmental Constraint |
| Power Limit (W) | Restricts maximum GPU power (300W–700W) | Control Constraint (Action 2) |
| Workload Intensity / Trend | Trajectory of AI compute load | Derived State ( |
The controller (backend/ml/agentic/thermal_agent.py) executes an autonomous 5-stage loop every telemetry tick:
-
1. Observe: Ingests 10-parameter telemetry, thermal velocity (
$dT/dt$ ), ambient temperature, and LSTM$T+5$ predictions. - 2. Reason: Evaluates junction thermal headroom to the 90°C TJunction Throttle Threshold and assesses ambient limits.
- 3. Plan: Formulates preemption & dynamic TDP cap reduction strategies.
-
4. Act: Dispatches discrete commands (
INCREASE_FAN_COOLING,REDUCE_POWER_LIMIT,MAINTAIN_SETTINGS). - 5. Verify: Performs closed-loop verification check on thermal deceleration.
- Knowledge Base: Contains official NVIDIA H100 SXM5 technical specs, HBM3 memory thresholds, liquid cooling flow equations, and emergency playbooks.
- Vector Search Index: Uses TF-IDF tokenization and Cosine Similarity vector matching (
backend/app/rag/vector_store.py). - Real-Time Telemetry RAG Diagnosis: Generates executive diagnostic reading reports based on live thermal dynamics.
- RAG Copilot Chat: Interactive chat assistant that answers user questions with direct document citations.
- Predicts multi-step ahead die temperatures (
$T+1 \dots T+5$ , 25 seconds ahead). - Calibrated to project future Stock BIOS unoptimized temperature trajectories so the AI Agent can act before heat spikes occur.
- Replays a dynamic 3-phase testing profile showcasing:
- Phase 1: Cool nominal baseline (~40°C).
- Phase 2: Heavy workload & ambient heatwave surge up to 95°C (triggering AI emergency preemption & TDP power limit capping).
- Phase 3: Cooldown and stabilization into the safe 65°C – 80°C equilibrium range.
- Real-time counters tracking accrued energy saved (kJ), liquid coolant conserved (Liters), and Arrhenius semiconductor mean time between failures (MTBF) lifespan extension (+Hours / %).
Backend (Machine Learning, RAG & Streaming)
- Framework: FastAPI
- Agentic AI & RL: Stable-Baselines3 (PPO Agent), Gymnasium (
H100CoolingEnv) - Deep Learning: PyTorch (LSTM Thermal Predictor)
- Vector RAG: Custom Cosine Similarity & TF-IDF Vector Index Engine
- Data & Math: Pandas, NumPy, Scikit-Learn, Joblib
- Streaming: WebSockets (
asyncio)
Frontend (Dashboard & Visualization)
- Framework: React 19 (Vite)
- Styling: Vanilla CSS3 (Glassmorphism & Sleek Dark Mode), Lucide Icons
- Charts: Recharts (handling real-time data buffering and asynchronous forecasting)
git clone https://github.com/yourusername/gpu-thermal-rl-optimizer.git
cd gpu-thermal-rl-optimizerNavigate to the backend, set up your Python environment, and generate the physical telemetry dataset:
# 1. Generate dynamic H100 10-parameter telemetry dataset
python generate_datasets.py
# 2. Train the PyTorch LSTM & PPO RL Agent (Saves weights to ml/lstm/weights and ml/rl/weights)
python backend/train_models.pySpin up the backend REST API & WebSocket stream:
cd backend
python -m uvicorn app.main:app --port 8000 --reloadOpen a new terminal window, navigate to the frontend directory, and start the UI:
cd frontend
npm install
npm run devVisit http://localhost:5173 to launch the live NVIDIA H100 Agentic AI Thermal Control Room!
For detailed technical explanations, mathematical formulations, LangChain/LangGraph concepts, and a 22-question technical interview guide, refer to:
This project is open-source under the MIT License.