What is it about?

Fine-tuning large language models requires substantial GPU and CPU memory. We introduce Tiny-Pipe, which jointly manages both types of memory and coordinates computation with data transfers. Experiments on a commodity server show that Tiny-Pipe reduces both GPU and CPU memory use while improving training throughput in most tested settings.

Featured Image

Why is it important?

Existing approaches often consume large amounts of both GPU and CPU memory, yet typically optimize only one of these resources. This leaves the other as a potential bottleneck, limiting large language model fine-tuning on resource-constrained servers. Tiny-Pipe addresses both memory constraints together while improving training throughput in most tested settings. This could help researchers and developers fine-tune larger models using the hardware already available to them.

Perspectives

For me, the key message of this work is that memory optimization and throughput optimization are not mutually exclusive: memory use can be reduced while training throughput is increased. Another key insight is that, when dividing a model into smaller parts to distribute the work across GPUs, making those parts too large or too small can slow down training. Choosing the right size is therefore essential for efficient training. I hope this work helps researchers and developers make better use of the hardware available to them and encourages further research into efficient large language model fine-tuning under practical resource constraints.

Yuquan Ding
University of Electronic Science and Technology of China

Read the Original

This page is a summary of: GPU and CPU Memory Co-Optimization in Heterogeneous Pipeline Parallelism for Efficient Large Language Model Fine-Tuning on Commodity Servers, ACM Transactions on Architecture and Code Optimization, August 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3839239.
You can read the full text:

Read

Contributors

The following have contributed to this page