Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission
arXiv:2505.11788v2 Announce Type: replace-cross Abstract: To support emerging language-based applications using dispersed and heterogeneous computing resources, the hybrid language model (HLM) offers a promising architecture, where an on-device small language model (SLM) generates draft tokens that are validated and corrected by a remote large language model (LLM). However, the original HLM suffers from substantial communication overhead, as the LLM requires the SLM to upload the full vocabulary distribution for each token. Moreover, both communication and computation resources are wasted when
The proliferation of language-based applications and dispersed computing resources necessitates more efficient communication protocols for hybrid AI models to scale effectively.
This research addresses a critical bottleneck in the deployment and efficiency of hybrid AI architectures, directly impacting the economic viability and user experience of advanced language models.
The proposed communication-efficient method allows for more scalable and less resource-intensive operation of hybrid language models by reducing data transmission requirements.
- · AI service providers
- · On-device AI hardware manufacturers
- · Edge computing platforms
- · Next-gen language model developers
- · Inefficient cloud-only language models
- · High-latency network providers
- · Legacy communication protocols
Reduced operational costs and improved performance for hybrid language models will accelerate their adoption and deployment across diverse applications.
The efficiency gains could lead to rapid innovation in AI applications that require real-time, on-device processing coupled with remote intelligence, like advanced AI agents.
Widespread adoption of these communication-efficient HLMs might further decentralize AI processing, potentially influencing the competitive landscape of AI infrastructure providers.
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Read at arXiv cs.LG