# PIPO: Pipelined Offloading for Efficient Inference on Consumer Devices

**Authors:** Yangyijian Liu, Jun Li, Wu-Jun Li  
**arXiv:** [2504.03664](https://arxiv.org/abs/2504.03664)  
**Date:** 2025-03-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2504.03664v1/

## Key points

- Demand for large language models (LLMs) has increased due to their strong capabilities in writing, conversation, and code generation.
- Deploying LLMs on consumer devices is challenging due to high memory and computation demands exceeding limited GPU memory.
- A novel framework called Pipelined Offloading (PIPO) has been proposed for efficient inference on consumer devices.
- PIPO features a fine-grained offloading pipeline with optimized data transfer and computation processes for high concurrency and efficient scheduling of inference tasks.
- Experimental results show that PIPO significantly increases GPU utilization from below 40% to over 90%, resulting in up to 3.1 times higher throughput.
- Effectiveness of PIPO demonstrated through experiments on a laptop equipped with an RTX3060 GPU with 6GB of memory.
- PIPO offers a promising solution for maximizing GPU utilization and improving the efficiency of inference tasks on resource-constrained devices.

## Summary

In recent years, the demand for large language models (LLMs) has significantly increased due to their strong capabilities in various applications such as writing, conversation, and code generation. However, deploying these LLMs on consumer devices poses a challenge due to their high memory and computation demands. This often exceeds the limited GPU memory available on such devices. To address this issue, a novel framework called Pipelined Offloading (PIPO) has been proposed for efficient inference on consumer devices. PIPO is designed with a fine-grained offloading pipeline that is complemented by optimized data transfer and computation processes. This design enables high concurrency and efficient scheduling for inference tasks. Experimental results have shown that compared to state-of-the-art baseline methods, PIPO significantly increases GPU utilization from below 40% to over 90%, resulting in up to 3.1 times higher throughput. The effectiveness of PIPO was demonstrated through experiments conducted on a laptop equipped with an RTX3060 GPU with 6GB of memory. The results highlight the potential of PIPO in maximizing GPU utilization and improving the efficiency of inference tasks on consumer devices. With its innovative approach to offloading and scheduling, PIPO offers a promising solution for overcoming the challenges associated with deploying large language models on resource-constrained devices.