arXiv:2602.10431v4 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment. Two complementary approaches have emerged to address these issues: token-adaptive layer execution, which reduces floating-point operations (FLOPs) by selectively bypassing layers, and quantization, which lowers memory footprint by reducing weight precision. However, naively integrating these techniques leads to additional accuracy degradation due to reduced redundancy in token-adaptive models. We propose QTALE (Quant
The increasing scale and resource demands of LLMs are pushing the limits of current computational infrastructure, making efficient deployment solutions like QTALE critical for widespread adoption.
Improving the efficiency of large language models through techniques like QTALE directly accelerates their deployment and accessibility, lowering the barrier to entry for advanced AI applications.
This research outlines a method to combine quantization and token-adaptive execution without significant accuracy degradation, making LLM deployment more memory and computationally efficient.
- · AI developers
- · Cloud computing providers
- · Edge AI hardware manufacturers
- · Companies relying solely on high-precision, unoptimised LLMs
More powerful LLMs become accessible on less powerful hardware and with lower operational costs.
Increased LLM deployment across diverse applications and devices, including mobile and embedded systems.
The proliferation of context-aware, generative AI agents becomes more feasible due to reduced resource overhead.
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Read at arXiv cs.LG