Assessing Economic Viability: A Comparative Analysis of Total Cost of Ownership for Domain-Adapted Large Language Models versus State-of-the-art Counterparts in Chip Design Coding Assistance
<|reference_start|>Assessing Economic Viability: A Comparative Analysis of Total Cost of Ownership for Domain-Adapted Large Language Models versus State-of-the-art Counterparts in Chip Design Coding Assistance: This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to coding assistance for chip design. We examine the TCO and performance metrics of a domain-adaptive LLM, ChipNeMo, against two leading LLMs, Claude 3 Opus and ChatGPT-4 Turbo, to assess their efficacy in chip design coding generation. Through a detailed evaluation of the accuracy of the model, training methodologies, and operational expenditures, this study aims to provide stakeholders with critical information to select the most economically viable and performance-efficient solutions for their specific needs. Our results underscore the benefits of employing domain-adapted models, such as ChipNeMo, that demonstrate improved performance at significantly reduced costs compared to their general-purpose counterparts. In particular, we reveal the potential of domain-adapted LLMs to decrease TCO by approximately 90%-95%, with the cost advantages becoming increasingly evident as the deployment scale expands. With expansion of deployment, the cost benefits of ChipNeMo become more pronounced, making domain-adaptive LLMs an attractive option for organizations with substantial coding needs supported by LLMs<|reference_end|>
@article{sharma2024assessing,
    title={Assessing Economic Viability: A Comparative Analysis of Total Cost of
  Ownership for Domain-Adapted Large Language Models versus State-of-the-art
  Counterparts in Chip Design Coding Assistance},
    author={Amit Sharma, Teodor-Dumitru Ene, Kishor Kunal, Mingjie Liu, Zafar
  Hasan and Haoxing Ren},
    journal={arXiv preprint arXiv:2404.08850},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08850},
    primaryClass={cs.AI cs.CE cs.LG}
}