  The rapid advancement of Large Language Models (LLMs) has significantly
impacted human-computer interaction, epitomized by the release of GPT-4o, which
introduced comprehensive multi-modality capabilities. In this paper, we first
explored the deployment strategies, economic considerations, and sustainability
challenges associated with the state-of-the-art LLMs. More specifically, we
discussed the deployment debate between Retrieval-Augmented Generation (RAG)
and fine-tuning, highlighting their respective advantages and limitations.
After that, we quantitatively analyzed the requirement of xPUs in training and
inference. Additionally, for the tokenomics of LLM services, we examined the
balance between performance and cost from the quality of experience (QoE)'s
perspective of end users. Lastly, we envisioned the future hybrid architecture
of LLM processing and its corresponding sustainability concerns, particularly
in the environmental carbon footprint impact. Through these discussions, we
provided a comprehensive overview of the operational and strategic
considerations essential for the responsible development and deployment of
LLMs.
