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Vision Language models (VLMs) have transformed Generative AI by enabling systems to interpret and respond to multi-modal data in real-time. While advancements in edge computing have made it possible to deploy smaller Large Language Models (LLMs) on smartphones and laptops, deploying competent VLMs on edge devices remains challenging due to their high computational demands. Furthermore, cloud-only deployments fail to utilize the evolving processing capabilities at the edge and limit responsiveness. This paper introduces a distributed architecture for VLMs that addresses these limitations by partitioning model components between edge devices and central servers. In this setup, vision components run on edge devices for immediate processing, while language generation of the VLM is handled by a centralized server, resulting in up to 33% improvement in throughput over traditional cloud-only solutions. Moreover, our approach enhances the computational efficiency of off-the-shelf VLM models without the need for model compression techniques. This work demonstrates the scalability and efficiency of a hybrid architecture for VLM deployment and contributes to the discussion on how distributed approaches can improve VLM performance. Index Terms—vision-language models (VLMs), edge computing, distributed computing, inference optimization, edge-cloud collaboration.
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This content will become publicly available on September 1, 2026
From concept to manufacturing: evaluating vision-language models for engineering design
Abstract Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated impressive capabilities in enabling this shift. Yet, with text as their only input modality, they cannot leverage the large body of visual artifacts that engineers have used for centuries and are accustomed to. This gap is addressed with the release of multimodal vision-language models (VLMs), such as GPT-4V, enabling AI to impact many more types of tasks. Our work presents a comprehensive evaluation of VLMs across a spectrum of engineering design tasks, categorized into four main areas: Conceptual Design, System-Level and Detailed Design, Manufacturing and Inspection, and Engineering Education Tasks. Specifically in this paper, we assess the capabilities of two VLMs, GPT-4V and LLaVA 1.6 34B, in design tasks such as sketch similarity analysis, CAD generation, topology optimization, manufacturability assessment, and engineering textbook problems. Through this structured evaluation, we not only explore VLMs’ proficiency in handling complex design challenges but also identify their limitations in complex engineering design applications. Our research establishes a foundation for future assessments of vision language models. It also contributes a set of benchmark testing datasets, with more than 1000 queries, for ongoing advancements and applications in this field.
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- Award ID(s):
- 2231254
- PAR ID:
- 10635107
- Publisher / Repository:
- Artificial Intelligence Review, Springer Nature
- Date Published:
- Journal Name:
- Artificial Intelligence Review
- Volume:
- 58
- Issue:
- 9
- ISSN:
- 1573-7462
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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