DeInfer: Efficient Parallel Inferencing for Decomposed Large Language Models

arXiv:2604.17709v2 Announce Type: replace Abstract: Existing works on large language model (LLM) decomposition mainly focus on improving performance on downstream tasks, but they ignore the poor parallel inference performance when trying to scale up the model size. To mitigate this important performance issue, this paper introduces DeInfer, a high-performance inference system dedicated to parallel inference of decomposed LLMs. It consists of multiple optimizations to maximize performance and be compatible with state-of-the-art optimization techniques. Extensive experiments are carried out to e

Source: arXiv cs.CL — read the full report at the original publisher.

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