Independent AI Researcher | Energy Efficiency & Sustainable Computing
| Asset | Type | Impact | Link |
|---|---|---|---|
| 🤗 HuggingFace Optimum Integration | Official Documentation | Trusted by thousands of HF developers | View Docs → |
| 📊 Complete Energy Dataset | Research Benchmark | 360+ configurations, 5 precision methods | Explore Data → |
| 🦾 EcoCompute AI Assistant | Interactive Tool | Conversational energy advisor on ClawHub | Try EcoCompute → |
| 🏛️ MLCommons Power WG Discussion | Industry Recognition | Invited to contribute to MLPerf power measurement standards | View Discussion → |
Quantization only saves energy for models > 3.2–4.6B parameters.
For smaller models, FP16 is actually more energy-efficient.
— Measured on RTX 4090D, RTX 5090, A800 with NVML power sampling.
This finding challenges the default assumption that "quantize everything = green." Our benchmark data is open and reproducible.
Key Findings:
- NF4 crossover: 3.2–3.9B parameters (hardware-dependent)
- INT8 crossover: 4.0–4.6B parameters (hardware-dependent)
- Below threshold: Quantization adds 25–55% energy overhead
- Above threshold: Quantization saves 15–23% energy
| 🌐 Live Demo | ecocompute-dynamic-eval → |
| 📊 What it does | Compare AI models by Accuracy × Cost × Carbon in one dashboard |
| ⚡ Data source | Real GPU benchmarks — PyTorch 2.10 + CUDA 12.8, 10 runs per config |
| Achievement | Details |
|---|---|
| 🤗 HuggingFace Official | Quantization energy findings integrated into Optimum documentation |
| 🏛️ MLCommons Invited | Contributing to MLPerf Power Working Group on quantization energy metrics |
| 📊 Open Dataset | 360 configurations, 270 analyzed + 90 FP8 reserved for future work |
| 🌍 Zenodo Archive | Permanent DOI: 10.5281/zenodo.18900289 |
| 📝 Research Paper | "When Does Quantization Save Energy?" — arXiv submission in progress |
- ✅ HuggingFace Integration — Official documentation published
- ✅ MLCommons Engagement — Invited to Power Working Group
- 🔄 arXiv Publication — Seeking endorsement for cs.LG submission
- 🛡️ VS Code Extension — Real-time energy linting before code merges
- 🤝 Enterprise Pilots — Seeking design partners for carbon-aware CI/CD
I'm looking for design partners, early adopters, arXiv endorsers, and grant sponsors to take EcoCompute from research to production.
| Action | Link |
|---|---|
| ⭐ Star the repo | quantization-energy-crossover |
| 🌐 Try the demo | Live Dashboard → |
| 📧 arXiv Endorsement | Email me → |
| 🤝 Become a design partner | Email me → |
| 💼 Invest / Grant | Email me → |
- Research Paper: "When Does Quantization Save Energy? Empirical Analysis of the Energy-Efficiency Crossover Effect Across GPU Generations"
- Dataset: GitHub | Zenodo DOI
- HuggingFace Docs: Optimum Energy Efficiency Guide
- MLCommons Discussion: Issue #2558
🌍 Making AI development more sustainable, one model at a time.