2024/07/10 by Pujiang He, He, Pujiang, Shan Zhou +17 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #FOS: Computer and information sciences #Inference #Parallel computing #Programming language #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2407.07304
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Large language models (LLMs) have shown exceptional performance and vast potential across diverse tasks. However, the deployment of LLMs with high performance in low-resource environments has garnered significant attention in the industry. When GPU hardware resources are limited, we can explore alternative options on CPUs. To mitigate the financial burden and alleviate constraints imposed by hardware resources, optimizing inference performance is necessary. In this paper, we introduce an easily deployable inference performance optimization solution aimed at accelerating LLMs on CPUs. In this solution, we implement an effective way to reduce the KV cache size while ensuring precision. We propose a distributed inference optimization approach and implement it based on oneAPI Collective Communications Library. Furthermore, we propose optimization approaches for LLMs on CPU, and conduct tailored optimizations for the most commonly used models. The code is open-sourced at https://github.com/intel/xFasterTransformer.