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.

通过引入层级分布式的大型语言模型（LLM）架构，提高 LLM 在异构计算平台上的可访问性和可部署性，实现按需访问和定制化服务，并在用户与应用需求之间取得最佳权衡，推动人工智能技术的进步。