# LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

arXiv: https://arxiv.org/abs/2403.06504 · HTML: https://mlsanity.com/paper/2403.06504/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2024.

## Built on by (most cited first, 10 of 10)

- [Standardization of Psychiatric Diagnoses -- Role of Fine-tuned LLM Consortium and OpenAI-gpt-oss Reasoning LLM Enabled Decision Support System](https://mlsanity.com/paper/2510.25588/) (2025)
- [Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order](https://mlsanity.com/paper/2506.04430/) (2025)
- [Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System](https://mlsanity.com/paper/2508.12473/) (2025)
- [Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains](https://mlsanity.com/paper/2604.05987/) (2026)
- [MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall](https://mlsanity.com/paper/2509.02480/) (2025)
- [Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling](https://mlsanity.com/paper/2602.13659/) (2026)
- [An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU](https://mlsanity.com/paper/2603.16428/) (2026)
- [Efficient Training on Multiple Consumer GPUs with RoundPipe](https://mlsanity.com/paper/2604.27085/) (2026)
- [Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments](https://mlsanity.com/paper/2605.16269/) (2026)
- [BaseCamp --- An Agentic AI Framework for Automating DNA Sequencing Data Pipelines](https://mlsanity.com/paper/2609.28557/) (2026)

## Abstract (arXiv)

Nowadays, AI researchers become more and more interested in fine-tuning a pre-trained LLM, whose size has grown to up to over 100B parameters, for their downstream tasks. One approach to fine-tune such huge models is to aggregate device memory from many GPUs. However, this approach introduces prohibitive costs for most data scientists with a limited budget for high-end GPU servers. In this paper, we focus on LLM fine-tuning on a single consumer-grade GPU in a commodity server with limited main memory capacity, which is accessible to most AI researchers. In such a scenario, existing offloading-based methods fail to fine-tune an LLM efficiently due to a lack of holistic intra-server tensor movement management. To this end, we present LoHan, a low-cost, high-performance deep learning training framework that enables efficient 100B-scale model fine-tuning on a commodity server with a consumer-grade GPU and limited main memory capacity. The key idea is to add holistic offloading traffic as an optimization dimension for 1)active gradient offloading, and 2)holistic traffic-aware activation swapping mechanism. The experimental results show that 1)LoHan is the first to fine-tune a 175B model on an RTX 4090 and 256 GB main memory, 2)LoHan achieves 2.32x throughput than the state-of-the-art baselines when fine-tuning a small 13B model, and 3)LoHan enables a cheap low-end consumer GPU to have higher cost-effectiveness than a DGX-A100 cluster when fine-tuning a 175B model.