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
arxiv(2024)
摘要
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
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
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