mlsanityData to 7 Oct 2026

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

arXiv 2408.04693 ↗First posted 8 Aug 2024cs.CL cs.AI cs.LGcited by 48

On the map: Mixture-of-experts → Efficient MoE training and inference systems

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.

Key facts. Cited by 48 papers in the map (data through 7 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.

Builds on

19 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Tutel: Adaptive Mixture-of-Experts at Scale7 Jun

Other references · 18

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  4. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  5. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  6. 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,551
  7. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  8. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  9. 2016Training Deep Nets with Sublinear Memory Costcited by 825
  10. 2022Mixture-of-Experts with Expert Choice Routingcited by 611
  11. 2024GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projectioncited by 447
  12. 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 317
6 more
  1. 2022SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapterscited by 83
  2. 2023LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugincited by 82
  3. 2024Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Modelscited by 41
  4. 2024BlackMamba: Mixture of Experts for State-Space Modelscited by 37
  5. 2024MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuningcited by 31
  6. 2023Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Modelscited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 29; 2026: 12.

48 papers.

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  5. 2024Exploring Gen-AI applications in building research and industry: A reviewcited by 2
  6. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmapcited by 2
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  8. 2025Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generationcited by 2
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  10. 2026Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphscited by 2
  11. 2025Collaboration is all you need: LLM Assisted Safe Code Translationcited by 1
  12. 2025LLM for Complex Reasoning Task: An Exploratory Study in Fermi Problemscited by 1
  13. 2025Command-V: Pasting LLM Behaviors via Activation Profilescited by 1
  14. 2025eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computingcited by 1
  15. 2025Multi-Model Synthetic Training for Mission-Critical Small Language Modelscited by 1
  16. 2025Advances in Large Language Models for Medicinecited by 1
  17. 2025Building Domain-Specific Small Language Models via Guided Data Generationcited by 1
  18. 2026Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Modelscited by 1
  19. 2026Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communitiescited by 1
  20. 2024Software Performance Engineering for Foundation Model-Powered Softwarecited by 0
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  23. 2025Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Taskscited by 0
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  25. 2025SVAgent: AI Agent for Hardware Security Verification Assertioncited by 0
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  28. 2025LOCUS: A System and Method for Low-Cost Customization for Universal Specializationcited by 0
  29. 2026LLM-Augmented Knowledge Base Construction For Root Cause Analysiscited by 0
  30. 2026An empirical study of LoRA-based fine-tuning of large language models for automated test case generationcited by 0
  31. 2026A Locally Deployed RAG-Based Academic Advising System for Course Selectioncited by 0
  32. 2026BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehensioncited by 0
  33. 2026Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Formscited by 0
  34. 2026Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUscited by 0
  35. 2026Position: AI Leaderboards Are Underserving the Global South: A Case Study from Indiacited by 0
  36. 2026Merging Cyber Threat Intelligence Through Retrieval-Augmented Generation and Small Language Models for Rich Threat Representationcited by 0

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