An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.

To address this challenge and democratize LLM fine-tuning, we present SlideFormer, a novel system designed for single-GPU environments.

Our innovations are: (1) A lightweight asynchronous engine that treats the GPU as a sliding window and overlaps GPU computation with CPU updates and multi-tier I/O.

(2) A highly efficient heterogeneous memory management scheme significantly reduces peak memory usage.

(3) Optimized Triton kernels to solve key bottlenecks and integrated advanced I/O.

This collaborative design enables fine-tuning of the latest 123B+ models on a single RTX 4090, supporting up to 8x larger batch sizes and 6x larger models.

In evaluations, SlideFormer achieves 1.40x to 6.27x higher throughput while roughly halving CPU/GPU memory usage compared to baselines, sustaining >95% peak performance on both NVIDIA and AMD this http URL code is available at this https URL.