FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization (2025.findings-emnlp)
Copied to clipboard
Fangxin Liu, Zongwu Wang, Jinhong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan
| Challenge: | Existing methods for quantization of large language models struggle to adapt to dynamic workloads. |
| Approach: | a new framework optimizes the trade-off between inference speed and accuracy . FlexQuant enables fine-grained, layer-wise mixed-precision quantization . |
| Outcome: | a new framework optimizes the trade-off between inference speed and accuracy . it achieves a 1.3 speedup across diverse language tasks with negligible accuracy loss . |
Similar Papers
Beyond Dynamic Quantization: An Efficient Static Hierarchical Mix-precision Framework for Near-Lossless LLM Compression (2025.emnlp-industry)
Copied to clipboard
| 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. |
A Comprehensive Evaluation of Quantization Strategies for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear. |
| Approach: | They propose a framework to evaluate quantized large language models using four dimensions . they propose to reduce the bits needed for model weights or activations with minimal performance loss . |
| Outcome: | The proposed framework can retain comparable performance to non-quantized LLMs on most benchmarks. |
pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for building efficient large language models with sub 2-bit weights are lacking in accuracy and scalability. |
| Approach: | They propose a method that decouples parameters by splitting linear layers into two specialized branches. |
| Outcome: | The proposed method achieves state-of-the-art performance in extremely low-bit quantization. |
QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Structured pruning can reduce model size but results in significant accuracy degradation . quantization and pruning increase the difficulty of fine-tuning, requiring a more refined quantization scheme. |
| Approach: | They propose a structured pruning framework followed by a layer-wise mixed-precision quantization scheme to reduce model memory consumption during fine-tuning and inference. |
| Outcome: | Experiments on benchmark datasets show that QPruner outperforms existing methods in memory savings while maintaining or improving model performance. |
MobileQuant: Mobile-friendly Quantization for On-device Language Models (2024.findings-emnlp)
Copied to clipboard
Fuwen Tan, Royson Lee, Łukasz Dudziak, Shell Xu Hu, Sourav Bhattacharya, Timothy Hospedales, Georgios Tzimiropoulos, Brais Martinez
| Challenge: | Large language models (LLMs) have revolutionized language processing, but deployment on edge devices is costly in terms of memory, computation and energy. |
| Approach: | They propose to reduce the number of bits used to represent weights and activations . they propose to use 8-bit activations to enable LLMs to fully exploit mobile-friendly hardware . |
| Outcome: | The proposed method reduces the number of bits used to represent weights and activations . 8-bit activations are attractive for on-device deployment as they would exploit mobile-friendly hardware . |
MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to optimize large language models for long-context inference are inefficient and consume memory. |
| Approach: | They propose a mixed-precision quantization method via mixture of experts that inputs tokens into router chunk by chunk to reduce inference overhead. |
| Outcome: | The proposed method outperforms state-of-the-art KV cache quantization methods on multiple benchmark datasets. |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for post-training quantization (PTQ) are limited by the complexity of the quantization parameter and performance degradations when tested on unseen datasets. |
| Approach: | They propose a learnable smooth-based PTQ framework that allows for rapid adaptation during testing. |
| Outcome: | The proposed framework improves performance on unseen datasets and reduces memory constraints. |
XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. however, their extensive memory requirements present significant challenges for deployment in resource-constrained environments. |
| Approach: | They propose a training-free framework that achieves ultra-low equivalent bit-width KV cache quantization. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on TruthfulQA and LongBench. |
QEFT: Quantization for Efficient Fine-Tuning of LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to optimize inference and fine-tuning for large language models have failed to improve all aspects of the process. |
| Approach: | They propose a new technique that accelerates both inference and fine-tuning while using fewer resources. |
| Outcome: | The proposed technique accelerates both inference and fine-tuning while using fewer resources. |
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | NVFP4 supports fine-grained block isolation, 4-bit quantization errors and mixed-precision approaches . ARCQuant boosts NVFO4 performance via Augmented Residual Channels . |
| Approach: | They propose a framework that boosts NVFP4 performance via Augmented Residual Channels. |
| Outcome: | ARCQuant boosts NVFP4 performance via Augmented Residual Channels . the proposed framework achieves state-of-the-art accuracy comparable to full-precision baselines compared to FP16 . |