QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models

TL;DR

This paper introduces QUIK, a hybrid 4-bit quantization method for large language models that significantly reduces inference costs while maintaining accuracy, enabling faster and more efficient generative AI applications.

Contribution

QUIK is the first hybrid 4-bit quantization approach that effectively compresses weights and activations for large LLMs, achieving practical speedups and high accuracy.

Findings

Up to 3.4x throughput improvement over FP16

Effective quantization for models like LLaMA, OPT, Falcon

Successful inference with 2:4 sparsity and quantization

Abstract

Large Language Models (LLMs) from the GPT family have become extremely popular, leading to a race towards reducing their inference costs to allow for efficient local computation. Yet, the vast majority of existing work focuses on weight-only quantization, which can reduce runtime costs in the memory-bound one-token-at-a-time generative setting, but does not address them in compute-bound scenarios, such as batched inference or prompt processing. In this paper, we address the general quantization problem, where both weights and activations should be quantized. We show, for the first time, that the majority of inference computations for large generative models such as LLaMA, OPT, and Falcon can be performed with both weights and activations being cast to 4 bits, in a way that leads to practical speedups, while at the same time maintaining good accuracy. We achieve this via a hybrid quantization strategy called QUIK, which compresses most of the weights and activations to 4-bit, while keeping some outlier weights and activations in higher-precision. The key feature of our scheme is that it is designed with computational efficiency in mind: we provide GPU kernels matching the QUIK format with highly-efficient layer-wise runtimes, which lead to practical end-to-end throughput improvements of up to 3.4x relative to FP16 execution. We provide detailed studies for models from the OPT, LLaMA-2 and Falcon families, as well as a first instance of accurate inference using quantization plus 2:4 sparsity. Code is available at: https://github.com/IST-DASLab/QUIK.