EcoCompute: Energy Efficiency Benchmark for Quantized Language Models

## Complete Benchmark Dataset Systematic energy efficiency measurements for quantized language models across 0.5B-14B parameters on NVIDIA Ada Lovelace (RTX 4090D), Blackwell (RTX 5090), and Ampere (A800 80GB) architectures. **113+ configurations** covering five precision methods: FP16, NF4, INT8 (default), INT8 (pure bnb), and FP8. ### What's Included - Complete metadata and experimental configurations - Raw energy measurements (RTX 4090D, RTX 5090, A800 80GB) - Model coverage: Qwen2, TinyLlama, Mistral, Yi-1.5 - Data quality: CV < 2%, n=2 repeated trials ### Key Findings - Small-Model Quantization Paradox: +25-56% energy for models