CTT is a compression pipeline for LLMs that achieves up to 49x memory reduction, 10x faster inference, 81% lower CO2 emissions, and retains 68-98% accuracy on code clone detection, summarization, and generation tasks.
The Escalating AI’s Energy Demands and the Imperative Need for Sustainable Solutions,
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Empirical comparison shows smaller open-weight LLMs achieve strong performance on everyday work tasks, supporting task-aware selection over always using the largest models for sustainability and cost reasons.
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Carbon-Taxed Transformers: A Green Compression Pipeline for Overgrown Language Models
CTT is a compression pipeline for LLMs that achieves up to 49x memory reduction, 10x faster inference, 81% lower CO2 emissions, and retains 68-98% accuracy on code clone detection, summarization, and generation tasks.
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Sustainability via LLM Right-sizing
Empirical comparison shows smaller open-weight LLMs achieve strong performance on everyday work tasks, supporting task-aware selection over always using the largest models for sustainability and cost reasons.