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

本研究通过比较分析了专用领域适应的大型语言模型（LLM）与最先进的 LLM 之间的总拥有成本和性能，重点关注与芯片设计相关的编码辅助任务。我们评估了一个专用领域适应的 LLM 模型 ChipNeMo 与两个领先的 LLM 模型 Claude 3 Opus 和 ChatGPT-4 Turbo 的 TCO 和性能指标，以评估它们在芯片设计编码生成方面的有效性，旨在为利益相关者提供关键的信息，以选择最经济有效和性能高效的解决方案以满足其特定需求。我们的结果强调了应用专用领域适应模型（如 ChipNeMo）的优势，它们在显著降低成本的同时提高了性能，尤其是我们揭示了专用领域适应 LLM 降低 TCO 约 90%-95% 的潜力，成本优势在部署规模扩大时变得越发显著，使专用领域适应 LLM 成为具有大量 LLM 支持编码需求的组织的有吸引力的选择。

评估经济可行性：领域适应大型语言模型与最先进对手在芯片设计编码辅助中总拥有成本的比较分析

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