LiveMind: Low-latency Large Language Models with Simultaneous Inference
Authors: name: Chuangtao Chen, name: Grace Li Zhang, name: Xunzhao Yin, name: Cheng Zhuo, name: Ulf Schlichtmann, name: Bing Li

In this paper, we introduce a novel low-latency inference framework for large
language models (LLMs) inference which enables LLMs to perform inferences with
incomplete prompts. By reallocating computational processes to prompt input
phase, we achieve a substantial reduction in latency, thereby significantly
enhancing the interactive experience for users of LLMs. The framework adeptly
manages the visibility of the streaming prompt to the model, allowing it to
infer from incomplete prompts or await additional prompts. Compared with
traditional inference methods that utilize complete prompts, our approach
demonstrates an average reduction of 59% in response latency on the MMLU-Pro
dataset, while maintaining comparable accuracy. Additionally, our framework
facilitates collaborative inference and output across different models. By
employing an LLM for inference and a small language model (SLM) for output, we
achieve an average 68% reduction in response latency, alongside a 5.5%
improvement in accuracy on the MMLU-Pro dataset compared with the SLM baseline.
For long prompts exceeding 20 sentences, the response latency can be reduced by
up to 93%.