arXiv:2604.18396v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency. Early Exit has emerged as a promising solution to accelerate inference by dynamically bypassing redundant layers. However, in decoder-only architectures, the efficiency of Early Exit is severely bottlenecked by the KV Cache Absence problem, where skipped layers fail to provide the necessary historical states for subsequent tokens. Existing solutions, such as recomputation or masking, either
The increasing scale and computational demands of Large Language Models necessitate continuous innovation in inference efficiency to sustain their usability and widespread adoption.
Improving LLM inference efficiency directly translates to lower operational costs, faster response times, and broader accessibility for AI applications, which impacts sectors reliant on these models.
This research suggests a method to significantly reduce the 'KV Cache Absence' bottleneck in decoder-only LLMs, potentially making early exit strategies more viable and widely adopted for accelerating inference.
- · AI developers
- · Cloud providers
- · Users of LLM-powered applications
- · Developers of less efficient LLM architectures
Widespread adoption of 'River-LLM' or similar techniques could lead to a noticeable reduction in LLM inference costs and latency.
Lower compute costs could enable novel LLM applications or make existing ones financially viable, broadening the scope of AI integration.
Increased accessibility and reduced operational overhead of LLMs might accelerate the development and deployment of AI agents across various industries.
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Read at arXiv cs.CL