Efficient LLM Inference On Cpus

Shen Haihao, Chang Hanwen, Dong Bo, Luo Yu, Meng Hengyu. Arxiv 2023

[Paper] [Code]    
Efficiency And Optimization GPT Has Code Model Architecture Pretraining Methods Quantization Reinforcement Learning Transformer

Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the astronomical amount of model parameters, which requires a demand for large memory capacity and high memory bandwidth. In this paper, we propose an effective approach that can make the deployment of LLMs more efficiently. We support an automatic INT4 weight-only quantization flow and design a special LLM runtime with highly-optimized kernels to accelerate the LLM inference on CPUs. We demonstrate the general applicability of our approach on popular LLMs including Llama2, Llama, GPT-NeoX, and showcase the extreme inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-transformers.

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