LLMs as On-demand Customizable Service
Souvika Sarkar Mohammad Fakhruddin Babar Monowar Hasan Shubhra Kanti Karmaker
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.