# Smaller, Smarter, Closer: The Edge of Collaborative Generative AI

arXiv: https://arxiv.org/abs/2505.16499 · HTML: https://mlsanity.com/paper/2505.16499/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 10 of 10)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://mlsanity.com/paper/2306.00978/) (2023)
- [LLM-Pruner: On the Structural Pruning of Large Language Models](https://mlsanity.com/paper/2305.11627/) (2023)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [MELTing point: Mobile Evaluation of Language Transformers](https://mlsanity.com/paper/2403.12844/) (2024)
- [The Future of Consumer Edge-AI Computing](https://mlsanity.com/paper/2210.10514/) (2022)
- [Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning](https://mlsanity.com/paper/2404.13082/) (2024)
- [VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework](https://mlsanity.com/paper/2406.13399/) (2024)
- [Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors](https://mlsanity.com/paper/2406.17163/) (2024)
- [The Internet of Things in the Era of Generative AI: Vision and Challenges](https://mlsanity.com/paper/2401.01923/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agentic TinyML for Intent-aware Handover in 6G Wireless Networks](https://mlsanity.com/paper/2508.09147/) (2025)
- [Trust-Aware Routing for Distributed Generative AI Inference at the Edge](https://mlsanity.com/paper/2603.28622/) (2026)

## Abstract (arXiv)

The rapid adoption of generative AI (GenAI), particularly Large Language Models (LLMs), has exposed critical limitations of cloud-centric deployments, including latency, cost, and privacy concerns. Meanwhile, Small Language Models (SLMs) are emerging as viable alternatives for resource-constrained edge environments, though they often lack the capabilities of their larger counterparts. This article explores the potential of collaborative inference systems that leverage both edge and cloud resources to address these challenges. By presenting distinct cooperation strategies alongside practical design principles and experimental insights, we offer actionable guidance for deploying GenAI across the computing continuum.