arXiv:2606.09514v1 Announce Type: new Abstract: Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Transformer blocks, but existing methods (i) provide limited control under user-specific compute budgets and (ii) typically fix the routing path, failing to adapt as the context grows during decoding. We propose Buddy, a budget-driven dynamic depth routing framework. Buddy uses a lightweight Decision Module to score intermediate layers conditioned on the input and deterministically executes the top-
The increasing computational demands and costs of large language models are pushing researchers to find more efficient inference methods, making such innovations timely.
Sophisticated readers should care because this development addresses a core limitation in LLM deployment, potentially making advanced AI more accessible and scalable across various applications.
The ability to dynamically adjust LLM depth based on real-time budgets and context changes represents a significant improvement over fixed-path pruning, offering more flexible and cost-effective AI inference.
- · AI service providers
- · Cloud infrastructure providers
- · LLM developers
- · Edge AI applications
- · Inefficient LLM architectures
Reduced operational costs and latency for large language model inference.
Accelerated deployment and broader adoption of complex AI models in resource-constrained environments.
Increased competition in AI model efficiency, potentially leading to a new wave of optimized AI hardware and software architectures.
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Read at arXiv cs.LG