UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling
arXiv:2605.30898v1 Announce Type: new Abstract: In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing approaches tackle this trade-off along two largely independent dimensions: model routing, which switches among models of different scales to match request complexity, and test-time scaling (TTS), which adjusts inference-time compute within a fixed model for fine-grained control. However, this decoupled design introduces inherent limitations. Model routing yields coarse-grained, discrete performance
The proliferation of increasingly large language models in real-world applications is accentuating the need to balance their computational demands with performance requirements.
Optimizing LLM inference efficiency directly impacts deployment costs, accessibility, and the ability to scale AI applications, which is critical for competitive advantage.
Current fragmented approaches to LLM optimization can now be unified, potentially leading to more efficient and adaptable AI systems in production environments.
- · AI service providers
- · Cloud infrastructure providers
- · LLM developers
- · AI-powered businesses
- · Inefficient LLM architectures
- · Companies with high AI compute costs
More cost-effective and performant deployment of large language models across various applications.
Accelerated adoption of sophisticated AI systems by a broader range of industries due to reduced operational overhead.
Increased demand for specialized hardware and software solutions that can efficiently manage dynamic AI workloads in real-time.
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Read at arXiv cs.AI