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Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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EcoCompute: Energy Efficiency Benchmark for Quantized Language Models

Authors: Hongping Zhang;

EcoCompute: Energy Efficiency Benchmark for Quantized Language Models

Abstract

## 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 <3B - Break-even threshold: 4.2B (Ada) / 5.2B (Blackwell) - 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 ### Try It Interactively **EcoCompute ClawHub Skill**: Query these benchmarks conversationally with the EcoLobster AI advisor. https://clawhub.ai/hongping-zh/ecocompute ### Documentation See [data/README.md](https://github.com/hongping-zh/ecocompute-ai/tree/main/data) for full documentation, citation format, and quick start guide. ### Interactive Dashboard https://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) --- 操作步骤

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average