LLMs as On-demand Customizable Service

• URL: http://arxiv.org/abs/2401.16577v1
• Date: Mon, 29 Jan 2024 21:24:10 GMT
• Title: LLMs as On-demand Customizable Service
• Authors: Souvika Sarkar, Mohammad Fakhruddin Babar, Monowar Hasan, Shubhra Kanti Karmaker (Santu)
• Abstract summary: We introduce a concept of hierarchical, distributed Large Language Models (LLMs) By introducing a "layered" approach, the proposed architecture enables on-demand accessibility to LLMs as a customizable service. We envision that the concept of hierarchical LLM will empower extensive, crowd-sourced user bases to harness the capabilities of LLMs.
• Score: 8.440060524215378
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable language understanding and generation capabilities. However, training, deploying, and accessing these models pose notable challenges, including resource-intensive demands, extended training durations, and scalability issues. To address these issues, we introduce a concept of hierarchical, distributed LLM architecture that aims at enhancing the accessibility and deployability of LLMs across heterogeneous computing platforms, including general-purpose computers (e.g., laptops) and IoT-style devices (e.g., embedded systems). By introducing a "layered" approach, the proposed architecture enables on-demand accessibility to LLMs as a customizable service. This approach also ensures optimal trade-offs between the available computational resources and the user's application needs. We envision that the concept of hierarchical LLM will empower extensive, crowd-sourced user bases to harness the capabilities of LLMs, thereby fostering advancements in AI technology in general.

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