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
Amit Sharma Teodor-Dumitru Ene Kishor Kunal Mingjie Liu Zafar Hasan Haoxing Ren
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