# Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments

arXiv: https://arxiv.org/abs/2512.22149 · HTML: https://mlsanity.com/paper/2512.22149/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 33 of its 37 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 33)

- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [ServerlessLLM: Low-Latency Serverless Inference for Large Language Models](https://mlsanity.com/paper/2401.14351/) (2024)
- [A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application](https://mlsanity.com/paper/2412.17481/) (2024)
- [A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud](https://mlsanity.com/paper/2205.15795/) (2022)
- [Self-Resource Allocation in Multi-Agent LLM Systems](https://mlsanity.com/paper/2504.02051/) (2025)
- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Torpor: GPU-Enabled Serverless Computing for Low-Latency, Resource-Efficient Inference](https://mlsanity.com/paper/2306.03622/) (2023)
- [AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure](https://mlsanity.com/paper/2504.03648/) (2025)
- [Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC](https://mlsanity.com/paper/2506.24045/) (2025)
- [Efficient and Scalable Agentic AI with Heterogeneous Systems](https://mlsanity.com/paper/2507.19635/) (2025)
- [KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling](https://mlsanity.com/paper/2507.07932/) (2025)
- [Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision](https://mlsanity.com/paper/2205.11913/) (2022)
- [AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments](https://mlsanity.com/paper/2504.00407/) (2025)
- [Towards Efficient and Practical GPU Multitasking in the Era of LLM](https://mlsanity.com/paper/2508.08448/) (2025)
- [PolyServe: Efficient Multi-SLO Serving at Scale](https://mlsanity.com/paper/2507.17769/) (2025)
- [DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-based Clusters](https://mlsanity.com/paper/2407.10169/) (2024)
- [HAS-GPU: Efficient Hybrid Auto-scaling with Fine-grained GPU Allocation for SLO-aware Serverless Inferences](https://mlsanity.com/paper/2505.01968/) (2025)
- [GPU Cluster Scheduling for Network-Sensitive Deep Learning](https://mlsanity.com/paper/2401.16492/) (2024)
- [Towards Fast Setup and High Throughput of GPU Serverless Computing](https://mlsanity.com/paper/2404.14691/) (2024)
- [ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless Platforms with Shareable GPUs](https://mlsanity.com/paper/2404.16812/) (2024)
- [Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach](https://mlsanity.com/paper/2405.08754/) (2024)
- [Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey](https://mlsanity.com/paper/2406.08115/) (2024)
- [Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing](https://mlsanity.com/paper/2407.13088/) (2024)
- [Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making](https://mlsanity.com/paper/2408.01000/) (2024)
- [FaaSTube: Optimizing GPU-oriented Data Transfer for Serverless Computing](https://mlsanity.com/paper/2411.01830/) (2024)
- [Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning](https://mlsanity.com/paper/2504.03682/) (2025)
- [Temporal-Aware GPU Resource Allocation for Distributed LLM Inference via Reinforcement Learning](https://mlsanity.com/paper/2507.10259/) (2025)
- [Cloud Native System for LLM Inference Serving](https://mlsanity.com/paper/2507.18007/) (2025)
- [AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms](https://mlsanity.com/paper/2412.02610/) (2024)

## Built on by (most cited first, 4 of 4)

- [LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems](https://mlsanity.com/paper/2601.19121/) (2026)
- [When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control](https://mlsanity.com/paper/2605.26418/) (2026)
- [RobustExplain: Evaluating Robustness of LLM-Based Explanation Agents for Recommendation](https://mlsanity.com/paper/2601.19120/) (2026)
- [Winning by Peeking: Unenforced Budgets and Test-Set Selection Inflate Short-Budget AutoML Comparisons](https://mlsanity.com/paper/2608.07303/) (2026)

## Abstract (arXiv)

Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficiently deploying these systems on serverless GPU platforms presents significant resource allocation challenges due to heterogeneous agent workloads, varying computational demands, and the need for cost-effective scaling. This paper presents an adaptive GPU resource allocation framework that achieves 85% latency reduction compared to round-robin scheduling while maintaining comparable throughput to static allocation, using an O(N) complexity algorithm for real-time adaptation. Our approach dynamically allocates GPU resources based on workload characteristics, agent priorities, and minimum resource requirements, enabling efficient utilization while maintaining quality of service. The framework addresses three key challenges: (1) heterogeneous computational demands across lightweight coordinators and heavyweight specialists, (2) dynamic workload fluctuations requiring millisecond-scale reallocation, and (3) capacity constraints in serverless environments. Through comprehensive simulations modeling realistic multi-agent workflows with four heterogeneous agents, we demonstrate that adaptive allocation outperforms static equal and round-robin strategies across latency, cost, and GPU utilization metrics. The framework provides a practical solution for deploying cost-efficient multi-agent AI systems on serverless GPU infrastructure.