Benchmarking Emerging Deep Learning Quantization Methods for Energy Efficiency
Bibliographic record
Abstract
In the era of generative artificial intelligence (AI), the quest for energy-efficient AI models is increasing. The increasing size of recent AI models has led to quantization techniques that reduce large models' computing and memory requirements. This study aims to compare the energy consumption of five quantization methods, viz. Gradient-based Post-Training Quantization (GPTQ),Activation-aware Weight Quantization (AWQ), GPT-Generated Model Language (GGML), GPT-Generated Unified Format (GGUF), and Bits and Bytes (BNB). We benchmark and analyze the energy efficiency of these commonly used quantization methods during inference. This preliminary exploration found that GGML and its successor GGUF were the most energy-efficient quantization methods. Our findings reveal significant variability in energy profiles across methods, challenging the notion that lower precision universally improves efficiency. The results underscore the need to benchmark quantization techniques from an energy perspective beyond just model compression. Our findings could guide the selection of models using quantization techniques and the development of new quantization techniques that prioritize energy efficiency, potentially leading to more environmentally friendly AI deployments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".