arXiv:2603.04444v3 Announce Type: replace-cross Abstract: As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- selecting the right model for each query at inference time -- has become a critical systems challenge. We present vLLM Semantic Router, a signal-driven decision routing framework for Mixture-of-Modality (MoM) model deployments. The central innovation is composable signal orchestration: the system extracts heterogeneous signal types from each request -- from sub-millisecond heuristic features (keywor
The rapid diversification of large language models across modalities and capabilities necessitates intelligent routing solutions to optimize performance and cost.
Efficient and intelligent routing of AI requests is critical for scaling AI deployments, managing costs, and maximizing the utility of diverse AI models.
The introduction of signal-driven decision routing frameworks enables more sophisticated and adaptive utilization of a growing array of specialized AI models.
- · AI platform providers
- · Enterprises deploying multimodal AI
- · AI infrastructure developers
- · Inefficient AI inference systems
- · Generic single-model AI solutions
Improved performance and cost-effectiveness of enterprise AI applications due to dynamic model selection.
Accelerated development and adoption of specialized, smaller 'expert' AI models, driving further AI differentiation.
The emergence of 'AI orchestration' as a distinct and critical layer within the overall AI stack, creating new software markets.
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Read at arXiv cs.AI