2025 Studies about LLM Quantization Performance Trade‐offs - chunhualiao/public-docs GitHub Wiki

Yes, several studies published in 2025 on arXiv have investigated the performance differences between original (full-precision) and quantized versions of large language models (LLMs), often focusing on aspects like accuracy, inference speed, memory usage, and task-specific impacts. These typically involve post-training quantization (PTQ) techniques, such as reducing weights from 16-bit or 32-bit to 8-bit, 4-bit, or lower. Below is a summary of key studies from 2025, based on their abstracts and findings:

• A study examined whether quantization affects LLM performance on long-context tasks, finding that 8-bit quantization generally preserves accuracy (with about a 0.8% drop), while 4-bit methods cause substantial losses, particularly for complex tasks involving extended sequences.
• Researchers interpreted the broader effects of quantization on LLMs, concluding that impacts vary by model and task, but no drastic overall changes were observed across evaluated scenarios.
• In the context of machine translation, a paper explored how quantization leads LLMs to "forget" certain knowledge, enabling significant reductions in model size and inference time but with potential degradation in translation quality for low-resource languages.
• A comprehensive evaluation of quantization techniques for LLMs assessed multiple PTQ methods, highlighting reductions in memory footprint and computational overhead, though with varying trade-offs in downstream task performance depending on the quantization bit-width.
• For diffusion-based language models, a systematic study of PTQ methods provided benchmarks across state-of-the-art approaches, showing how quantization influences generative quality and efficiency.
• An investigation into quantization's impact on LLM explainability and faithfulness found that lower-bit quantization can accelerate inference but may reduce the reliability of model explanations in interpretability tasks.
• Empirical work on quantization's effect on reasoning abilities in LLMs revealed that quantized models do not necessarily produce longer outputs as expected, and scaling model sizes or fine-tuning can mitigate some performance drops.
• A study on quantization's influence on safety and robustness evaluated techniques across models like LLaMA and Mistral, using benchmarks that included human evaluations to measure safety alignment and performance degradation.
• Systematic characterization of LLM quantization provided application-level insights, including latency and energy consumption impacts from different methods.

These studies collectively indicate that while quantization enables efficient deployment, it often introduces performance trade-offs that depend on the model architecture, quantization method, and evaluation tasks. For full details, refer to the linked arXiv papers.