Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation
📰 ArXiv cs.AI
Rethink fine-tuning for on-device LLM adaptation, as parameter efficiency doesn't guarantee memory efficiency
Action Steps
- Challenge the assumption that parameter efficiency equals memory efficiency
- Analyze the memory usage of intermediate tensors in PEFT methods like LoRA and IA3
- Consider alternative fine-tuning methods that optimize for memory efficiency
- Evaluate the trade-offs between parameter efficiency and memory efficiency in on-device LLM adaptation
- Develop new methods that balance parameter and memory efficiency for on-device deployment
Who Needs to Know This
ML engineers and researchers working on on-device LLM adaptation will benefit from this insight, as it challenges the common assumption that parameter efficiency equals memory efficiency
Key Insight
💡 Parameter efficiency does not guarantee memory efficiency in fine-tuning for on-device LLM adaptation
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🚨 Parameter efficiency ≠ memory efficiency in on-device LLM adaptation 🚨
Key Takeaways
Rethink fine-tuning for on-device LLM adaptation, as parameter efficiency doesn't guarantee memory efficiency
Full Article
Title: Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation
Abstract:
arXiv:2604.22783v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become the standard for adapting large language models (LLMs). In this work we challenge the wide-spread assumption that parameter efficiency equates memory efficiency and on-device adaptability. We show that this is not true - while methods like LoRA and IA3 significantly reduce trainable parameters, they remain bound by intermediate tensors that scale linearly with sequence length, often triggering out
Abstract:
arXiv:2604.22783v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become the standard for adapting large language models (LLMs). In this work we challenge the wide-spread assumption that parameter efficiency equates memory efficiency and on-device adaptability. We show that this is not true - while methods like LoRA and IA3 significantly reduce trainable parameters, they remain bound by intermediate tensors that scale linearly with sequence length, often triggering out
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