# Distributed Inference Performance Optimization for LLMs on CPUs

arXiv: https://arxiv.org/abs/2407.00029 · HTML: https://mlsanity.com/paper/2407.00029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2024.

## Built on by (most cited first, 3 of 3)

- [Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective](https://mlsanity.com/paper/2410.04466/) (2024)
- [Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2503.16585/) (2025)
- [Optimizing Distributed Deployment of Mixture-of-Experts Model Inference in Serverless Computing](https://mlsanity.com/paper/2501.05313/) (2025)

## Abstract (arXiv)

Large language models (LLMs) hold tremendous potential for addressing numerous real-world challenges, yet they typically demand significant computational resources and memory. Deploying LLMs onto a resource-limited hardware device with restricted memory capacity presents considerable challenges. Distributed computing emerges as a prevalent strategy to mitigate single-node memory constraints and expedite LLM inference performance. To reduce the hardware limitation burden, we proposed an efficient distributed inference optimization solution for LLMs on CPUs. We conduct experiments with the proposed solution on 5th Gen Intel Xeon Scalable Processors, and the result shows the time per output token for the LLM with 72B parameter is 140 ms/token, much faster than the average human reading speed about 200ms per token.