AI-Driven Predictive Load Orchestration for Distributed LLM Inference Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.17828171
This paper presents a novel framework for AI-driven predictive load orchestration specifically tailored for distributed Large Language Model (LLM) inference. As LLMs scale in size and complexity, deploying them across distributed computing environments becomes essential for meeting high throughput and low latency requirements. Traditional load balancing techniques often struggle with the dynamic and heterogeneous computational demands of LLM inference, leading to suboptimal resource utilization and increased response times. Our proposed approach leverages advanced artificial intelligence techniques, including machine learning for demand forecasting and reinforcement learning for dynamic resource allocation, to predict future inference loads and intelligently orchestrate computational resources across a cluster. We detail a methodology encompassing real-time telemetry collection, predictive modeling of token generation rates and model specific computational requirements, and a policy-driven orchestration engine. This framework aims to minimize inference latency, maximize GPU and CPU utilization, and ensure service reliability under fluctuating workloads. The paper discusses the architectural components, algorithmic considerations, and potential benefits of such an AI-driven system, highlighting its potential to significantly enhance the efficiency and scalability of large-scale LLM deployments.
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- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17828171
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7109092149