Computational Efficiency of OpenPangu-7B-MLA vs. LLaVA and Qwen-VL for Real-Time EchoMind Classification on Edge Devices

Abstract

Abstract: The advent of real-time large multimodal models (LMMs) like GPT-4o has sparked considerable interest in efficient LMMs. LMM frameworks typically encode visual inputs into vision tokens (continuous representations) and integrate them and textual instructions into the context of large language models (LLMs), where large-scale parameters and numerous context tokens (predominantly vision tokens) result in substantial computational overhead. Previous efforts towards efficient LMMs always focus on replacing the LLM backbone with smaller models, while neglecting the crucial issue of token quantity. I

Research Question

What is the computational efficiency of OpenPangu-7B-MLA versus state-of-the-art multimodal LLMs like LLaVA or Qwen-VL for real-time EchoMind classification on edge devices, measured by latency and throughput under varying input modalities (audio, text, image)?

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