Challenge: Quantization methods are available to solve the problem of high computational and storage costs for Large language models.
Approach: They propose an INT8 weight-activation quantization method that can achieve lossless accuracy.
Outcome: The proposed method can achieve lossless accuracy on OPT and LLaMA families.

Similar Papers

Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization (2023.emnlp-main)

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Challenge: Large language models (LLMs) are proficient in natural language processing tasks, but their deployment is limited by extensive parameter sizes and computational demands.
Approach: They propose a method to enhance computational efficiency in large language models by 4-bit weight and 8-bit activation quantization.
Outcome: The proposed techniques significantly boost task accuracies to levels comparable with full-precision models.
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices (2025.naacl-long)

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Challenge: Existing methods for quantizing weights and activations of large language models suffer from non-negligible accuracy drops, especially on massive multitask language understanding.
Approach: They propose a weight-activation quantization method that reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices.
Outcome: The proposed method reduces the complexity of the weight-activation quantization techniques while achieving high throughput and reducing inference costs.
Achieving binary weight and activation for LLMs using Post-Training Quantization (2025.findings-acl)

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Challenge: Existing methods for quantizing large language models suffer from performance degradation when weights are quantized to 1 bit.
Approach: They propose a post-training quantization framework with W(1+1)A(14) configuration . they propose utilizing Hessian-aware fine-grained grouping along with an EM-based quantization scheme .
Outcome: The proposed method surpasses state-of-the-art (SOTA) LLM quantization baselines on W2A4 across multiple tasks.
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other (2024.findings-naacl)

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Challenge: Emergent Large Language Models (LLMs) use extraordinary performance and powerful deduction capacity to discern from traditional language models.
Approach: They propose a method that uses weights to compensate quantization error and learnable singular value incremental (LSI) LSI is a technique that helps weights compensate each other conditioned on activation.
Outcome: The proposed method achieves state-of-the-art performance in diverse quantization settings, no matter in weight-only, weight-activation or extremely low bit scenarios.
PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models (2026.acl-srw)

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Challenge: Quantization Aware Training (QAT) is expensive to train and unscalable to large models.
Approach: They propose a parameter-efficient framework targeting per-channel 4-bit weight-activation quantization of large language models.
Outcome: The proposed framework preserves accuracy within 0.11 percentage points of the full-precision baseline on Llama-2-7B zero-shot tasks while training only 1.26% of total parameters.
ACBQ: Adaptive Cross-Block Quantization of Large Language Models (2026.acl-long)

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Challenge: Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization.
Approach: They propose a framework that addresses weight–activation joint quantization and extreme weight quantization.
Outcome: The proposed framework achieves superior performance under both W4A4 and highly aggressive W2 settings while incurring negligible additional computational overhead.
“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization (2025.acl-long)

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Challenge: despite popularity of large language model quantization, there are significant accuracy-performance trade-offs associated with quantization formats.
Approach: They evaluate popular quantization formats across academic benchmarks and real-world tasks . they also examine the difference in text generated by quantized models versus their uncompressed counterparts .
Outcome: The proposed format is lossless across all model scales and incurs low accuracy degradation when properly tuned.
DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large Language Models (2024.emnlp-industry)

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Challenge: Quantization-aware Training (QAT) is a popular technique for reducing memory usage and improving computational efficiency in large language models.
Approach: They propose a weight-decomposed low-rank quantization-aware training approach that integrates QAT with a group-specific quantization magnitude adjustment.
Outcome: The proposed method outperforms the state-of-the-art method on LLaMA and LLama2 models.
LLM-FP4: 4-Bit Floating-Point Quantized Transformers (2023.emnlp-main)

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Challenge: Existing quantization solutions are integer-based and struggle with bit widths below 8 bits.
Approach: They propose a method for quantizing weights and activations in large language models down to 4-bit floating-point values in a post-training manner.
Outcome: The proposed method outperforms existing methods on common sense zero-shot reasoning tasks by 12.7 points.
ApiQ: Finetuning of 2-Bit Quantized Large Language Model (2024.emnlp-main)

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Challenge: Memory-efficient finetuning of large language models (LLMs) has attracted huge attention with the increasing size of LLMs due to the constraints posed by GPU memory limitations and the effectiveness of these methods compared to full finetune.
Approach: They propose a memory-efficient finetuning framework called ApiQ to restore lost information from quantization by initializing LoRA components and quantizing weights of LLMs.
Outcome: The proposed framework maintains the original LLM’s activation precision while mitigating error propagation from shallower into deeper layers.

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