Researchers Develop Groundbreaking W4A8 Post-Training Quantization Method for Efficient Deployment of Large Language Models

Researchers from Meituan and Nanjing University have developed a novel post-training quantization method for large language models (LLMs) that optimally utilizes computational resources without compromising performance. The study, published in a recent scientific paper, addresses the significant challenges posed by the substantial parameter size of LLMs for deployment.

The proposed W4A8 post-training quantization method combines the advantages of two existing recipes, W8A8 and W4A16, and leverages the benefits of 4-bit weight quantization and the acceleration due to 8-bit matrix computation. This innovative approach involves layerwise activation quantization strategies, featuring a novel logarithmic equalization for most intractable layers, and combines them with fine-grained weight quantization. As a result, the necessity for further fine-tuning is eliminated, and state-of-the-art W4A8 quantized performance is achieved on standard benchmarks BLOOM, LLaMA, and LLaMA-2. This confirms that W4A8 quantization is achievable for the deployment of LLMs, fostering their wide-spreading real-world applications.

The authors also compared their method with several existing approaches, such as LLM.int8(), GPTQ, and AWQ, which have limitations like computational overhead, inability to leverage hardware acceleration, weight reordering, asymmetric quantization, and group-wise activation. The proposed fine-grained post-training quantization (FPTQ) method addresses these issues by employing a layerwise quantization strategy regarding disparate activation distributions, without relying on quantization-aware training (QAT) or distillation methods. This significantly simplifies the deployment pipeline without compromising the performance of LLMs.

In conclusion, the study presents a significant stride in the domain of LLM compression by introducing a novel post-training quantization approach that makes LLM inference more efficient without compromising performance. This method provides an effective deployable solution for LLMs without sacrificing their accuracy, although the authors acknowledge the potential for further exploration and refinement in this area. This groundbreaking work is expected to inspire future research endeavors aimed at making LLMs even more efficient and practical for real-world applications. For more details check out here.