The paper presents a carbon‑aware routing framework for function‑calling in large language models that distributes queries across a three‑tier edge‑cloud architecture. A lightweight k‑NN predictor estimates accuracy, delay, and power for each edge tier, and real‑time grid carbon intensity is used to route queries to the lowest‑emission tier that can execute them. Experiments on state‑of‑the‑art benchmarks show the framework matches cloud‑level accuracy while cutting operational carbon emissions by an average of four times.
arXiv:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...
arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
arXiv:2605. 23348v2 Announce Type: replace-cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up.
arXiv:2608.28726v1 Announce Type: new Abstract: The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant chal...
arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
arXiv:2602. 15423v4 Announce Type: replace-cross Abstract: As the burgeoning power requirements of sophisticated neural architectures escalate, the information retrieval community has recognized ecological sustainability as a pivotal priority that necessitates a fundamental paradigm shift in model design.
arXiv:2511. 07885v5 Announce Type: replace-cross Abstract: Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure.
arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires bala...
arXiv:2607. 22805v1 Announce Type: cross Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments.
arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
arXiv:2606. 14356v1 Announce Type: cross Abstract: Applications in the 3D Computing Continuum, which unifies edge, cloud, and space, require combining multiple AI tasks such as object detection, time-series analytics, and natural language processing into Compound AI systems.
The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.