Squeezing LLMs. Up to 16x compression ratio achieved without significant performance loss.
This paper proposes a unified framework for KV cache compression in LLMs, addressing memory constraints and improving inference efficiency.
https://arxiv.org/abs/2412.03131
🔍 Original Problem:
→ LLMs face memory constraints during inference due to large KV caches.
→ Existing compression methods are often model-specific and lack generalization.
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💡 Solution in this Paper:
→ The paper introduces a unified framework for KV cache compression in LLMs.
→ It combines three compression techniques: pruning, quantization, and low-rank factorization.
→ Pruning removes less important cache entries based on attention scores.
→ Quantization reduces the precision of cache values to save memory.
→ Low-rank factorization approximates the cache matrix with lower-dimensional representations.
→ The framework allows flexible combination of these techniques for optimal compression.
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🧠 Key Insights from this Paper:
→ Unified approach outperforms individual compression methods.
→ Compression techniques can be combined for better results.
→ Framework is adaptable to different LLM architectures.
→ Trade-off between compression ratio and model performance can be fine-tuned.
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📊 Results:
→ Up to 16x compression ratio achieved without significant performance loss.
→ 2-4x speedup in inference time observed across various LLM sizes.
→ Maintained 99% of original model performance in most cases.
