arXiv:2606.17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles. Selecting the appropriate LLM for each incoming task is critical for ensuring service quality and efficient resource utilization. However, model heterogeneity, stochastic and unknown performance characteristics, and time-varying task demands make static selection strategies inadequate. Real-world deployments often impose hard resource budgets such as monetary expenditure lim
The proliferation of diverse LLMs and their deployment in real-world inference systems necessitate dynamic selection strategies to manage performance, cost, and resource constraints effectively.
This research addresses a critical operational challenge in deploying LLMs, impacting efficiency, cost-effectiveness, and quality of service, especially as LLMs become foundational infrastructure.
The shift from static to dynamic, adaptive LLM selection allows for more efficient resource utilization and better performance guarantees in varied, real-time environments.
- · Cloud providers
- · AI-powered enterprises
- · Users of LLM applications
- · Edge computing infrastructure
- · Inefficient LLM deployment strategies
- · Enterprises with high compute waste
Reduced operational costs and improved application performance for LLM-dependent services.
Accelerated adoption of more complex and diverse LLM architectures due to better management tools.
Increased competition among LLM providers as selection mechanisms become more sophisticated in evaluating real-world performance.
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Read at arXiv cs.AI