Beyond Dynamic Quantization: An Efficient Static Hierarchical Mix-precision Framework for Near-Lossless LLM Compression (2025.emnlp-industry)
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| Challenge: | Existing methods for dynamic quantization are hardware-unfriendly and often lead to large quantization errors in static scenarios. |
| Approach: | They propose a Static Hierarchical Mix-precision Quantization method which quantifies both inter-layer and intra-layer sensitivity through unified derivations involving Hessian. |
| Outcome: | The proposed method achieves 75.58% on zero-shot reasoning tasks while yielding average speedup of 2.86. |
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| Challenge: | Existing methods for sub 2-bit quantization introduce an extra 1-bit or more per weight. |
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CoopQ: Cooperative Game Inspired Layerwise Mixed Precision Quantization for LLMs (2026.findings-acl)
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| Challenge: | Existing methods for large language models struggle when the average precision drops below four bits, limiting deployment on resourceconstrained devices such as mobiles, edge sensors, or standard GPUs. |
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| Challenge: | Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization. |
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