EcoCompute: Energy Efficiency Benchmark for Quantized Language Models (v1.1.0)

## 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), Ampere (A800 80GB), and Turing (Tesla T4) architectures.

**360+ configurations** covering five precision methods: FP16, NF4, INT8 (default), INT8 (pure bnb), and FP8.

### What's Included (v1.1.0)- Complete metadata and experimental configurations- Raw energy measurements (RTX 4090D, RTX 5090, A800 80GB, Tesla T4)- Model coverage: Qwen2, TinyLlama, Mistral, Yi-1.5- Data quality: CV < 2%, n=2 repeated trials- **NEW**: Tesla T4 (Turing architecture) data - 140 configurations- **NEW**: Cross-architecture validation chart (Turing vs Blackwell)- **NEW**: Detailed Tesla T4 experiment documentation

### Key Findings- Small-Model Quantization Paradox: +25-56% energy for models <3B- Break-even threshold: 4.2B (Ada) / 5.2B (Blackwell) / 3.4B (Turing)- INT8 default is 4.6x less efficient than NF4 for small models- FP8 Paradox: up to +701% energy overhead on RTX 5090 due to software immaturity- **NEW**: Cross-architecture validation confirms crossover threshold is architecture-independent

### Try It Interactively**EcoCompute ClawHub Skill**: Query these benchmarks conversationally with the EcoLobster AI advisor.https://clawhub.ai/hongping-zh/ecocompute

### DocumentationSee [data/README.md](https://github.com/hongping-zh/ecocompute-ai/tree/main/data) for full documentation, citation format, and quick start guide.

### Interactive Dashboardhttps://hongping-zh.github.io/ecocompute-dynamic-eval/

---

**License**: CC BY 4.0 | **Citation**: See data/README.md

### Community Adoption- Referenced in [HuggingFace Optimum official documentation](https://huggingface.co/docs/optimum/concept_guides/quantization) ([PR #2410](https://github.com/huggingface/optimum/pull/2410), merged Mar 2026)- Dataset mirrored on [HuggingFace Hub](https://huggingface.co/datasets/hongpingzhang/ecocompute-energy-efficiency)- Available as interactive AI skill on [ClawHub](https://clawhub.ai/hongping-zh/ecocompute)- FP8 energy anomaly confirmed by [torchao maintainers](https://github.com/pytorch/ao/issues/4094)- Related contributions: [bitsandbytes PR #1882](https://github.com/bitsandbytes-foundation/bitsandbytes/pull/1882), [Transformers PR #44407](https://github.com/huggingface/transformers/pull/44407)