arXiv:2606.11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these methods do not consider the runtime state of the serving infrastructure. On shared GPU clusters under concurrent load, this infrastructure blindness causes systematic resource underutilization: preferred models accumulate deep request queues while equally capable alternatives sit idle. In multi-agent pipelines, where each query triggers multiple sequential model calls,
The increasing complexity and scale of multi-agent LLM systems, coupled with the rising costs and scarcity of GPU compute, necessitate more efficient resource orchestration solutions.
Efficient infrastructure orchestration for multi-agent AI systems can significantly reduce operational costs, maximize GPU utilization, and enable more complex and scalable AI applications.
The focus for multi-agent LLM orchestration shifts from solely task and model features to also include real-time infrastructure state, optimizing resource allocation dynamically.
- · Cloud providers
- · AI model developers
- · Companies deploying large-scale AI agents
- · GPU manufacturers
- · Inefficient AI orchestration platforms
- · Organizations with static resource allocation strategies
Improved performance and cost-efficiency for multi-agent AI systems due to better resource utilization.
Acceleration in the deployment and adoption of sophisticated AI agent workflows across industries.
Increased demand for advanced compute infrastructure management tools and expertise to handle dynamic AI workloads.
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Read at arXiv cs.AI