arXiv:2605.30736v1 Announce Type: new Abstract: The rapid development of large language models, each with distinct capabilities and inference costs, raises a practical deployment question: given an incoming request, which model should handle it? We present OrcaRouter, a production-oriented LLM router that combines a LinUCB-based contextual bandit over lexical and sentence-embedding features with a hybrid offline-online learning protocol. Offline, OrcaRouter obtains full-information feedback by evaluating each candidate model on a curated set of routing prompts, yielding a reward matrix used to
The proliferation of various LLMs with differing capabilities and costs necessitates efficient routing solutions to optimize deployment and resource utilization in real-world applications.
Efficient LLM routing is crucial for managing the cost and performance of large language model deployments, directly impacting the economic viability and scalability of AI applications.
This development offers a method to dynamically select the optimal LLM for a given task, improving efficiency and reducing operational expenses for AI-powered services.
- · AI-powered service providers
- · Cloud computing platforms
- · Developers of custom LLMs
- · Inefficient LLM deployment strategies
- · Developers of monolithic AI systems
- · Companies with high LLM inference costs
Reduced operational costs and improved performance for AI applications leveraging multiple LLMs.
Increased adoption and diversification of specialized LLMs as routing becomes more sophisticated and manageable.
Accelerated innovation in language models as the economic barriers to deploying diverse models are lowered.
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Read at arXiv cs.LG