# Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

arXiv: https://arxiv.org/abs/2408.04693 · HTML: https://mlsanity.com/paper/2408.04693/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Placed in Efficient MoE training and inference systems, one of 12 directions in Mixture-of-experts.
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Placed in

- Mixture-of-experts / [Efficient MoE training and inference systems](https://mlsanity.com/moe/efficient-moe-training-and-inference-systems/)

## Builds on (most cited first, 19 of 19)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://mlsanity.com/paper/2403.03507/) (2024)
- [LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2304.01933/) (2023)
- [Tutel: Adaptive Mixture-of-Experts at Scale](https://mlsanity.com/paper/2206.03382/) (2022)
- [SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters](https://mlsanity.com/paper/2210.04284/) (2022)
- [LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://mlsanity.com/paper/2312.09979/) (2023)
- [Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models](https://mlsanity.com/paper/2404.05567/) (2024)
- [BlackMamba: Mixture of Experts for State-Space Models](https://mlsanity.com/paper/2402.01771/) (2024)
- [MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2405.12130/) (2024)
- [Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models](https://mlsanity.com/paper/2311.03687/) (2023)

## Built on by (most cited first, 30 of 48)

- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [LLMs: A Game-Changer for Software Engineers?](https://mlsanity.com/paper/2411.00932/) (2024)
- [From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation](https://mlsanity.com/paper/2507.19364/) (2025)
- [Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins](https://mlsanity.com/paper/2503.21054/) (2025)
- [Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs](https://mlsanity.com/paper/2507.19334/) (2025)
- [Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading](https://mlsanity.com/paper/2410.21316/) (2024)
- [A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness](https://mlsanity.com/paper/2510.13890/) (2025)
- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)
- [Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering](https://mlsanity.com/paper/2506.00491/) (2025)
- [Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes](https://mlsanity.com/paper/2502.09690/) (2025)
- [Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating](https://mlsanity.com/paper/2505.19543/) (2025)
- [Accelerating Direct Preference Optimization with Prefix Sharing](https://mlsanity.com/paper/2410.20305/) (2024)
- [Investigating Task Arithmetic for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2505.00649/) (2025)
- [LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades](https://mlsanity.com/paper/2505.13515/) (2025)
- [Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour](https://mlsanity.com/paper/2511.02599/) (2025)
- [MeMo: Memory as a Model](https://mlsanity.com/paper/2605.15156/) (2026)
- [Exploring Gen-AI applications in building research and industry: A review](https://mlsanity.com/paper/2410.01098/) (2024)
- [From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap](https://mlsanity.com/paper/2410.20791/) (2024)
- [OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2502.18992/) (2025)
- [Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation](https://mlsanity.com/paper/2510.27080/) (2025)
- [Thinking While Speaking: Inference-Time Knowledge Transfer for Responsive and Intelligent Conversational Voice Agents](https://mlsanity.com/paper/2511.07397/) (2025)
- [Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2602.04019/) (2026)
- [Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2604.13979/) (2026)
- [Collaboration is all you need: LLM Assisted Safe Code Translation](https://mlsanity.com/paper/2503.11237/) (2025)
- [LLM for Complex Reasoning Task: An Exploratory Study in Fermi Problems](https://mlsanity.com/paper/2504.02671/) (2025)
- [Command-V: Pasting LLM Behaviors via Activation Profiles](https://mlsanity.com/paper/2506.19140/) (2025)
- [SVAgent: AI Agent for Hardware Security Verification Assertion](https://mlsanity.com/paper/2507.16203/) (2025)
- [eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing](https://mlsanity.com/paper/2508.10370/) (2025)
- [Multi-Model Synthetic Training for Mission-Critical Small Language Models](https://mlsanity.com/paper/2509.13047/) (2025)
- [Advances in Large Language Models for Medicine](https://mlsanity.com/paper/2509.18690/) (2025)

## Abstract (arXiv)

Due to the cost-prohibitive nature of training Large Language Models (LLMs), fine-tuning has emerged as an attractive alternative for specializing LLMs for specific tasks using limited compute resources in a cost-effective manner. In this paper, we characterize sparse Mixture of Experts (MoE) based LLM fine-tuning to understand their accuracy and runtime performance on a single GPU. Our evaluation provides unique insights into the training efficacy of sparse and dense versions of MoE models, as well as their runtime characteristics, including maximum batch size, execution time breakdown, end-to-end throughput, GPU hardware utilization, and load distribution. Our study identifies the optimization of the MoE layer as crucial for further improving the performance of LLM fine-tuning. Using our profiling results, we also develop and validate an analytical model to estimate the cost of LLM fine-tuning on the cloud. This model, based on parameters of the model and GPU architecture, estimates LLM throughput and the cost of training, aiding practitioners in industry and academia to budget the cost of fine-tuning a specific model.