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Efficient Foundation Model Inference Across the Computing Continuum

Objective

Foundation Models (FMs) represent a new class of AI paradigm that is trained on broad data and is adaptable to diverse downstream tasks across multiple modalities, including text, images, audio, video, and even time-series. They enable generative AI with zero-shot and few-shot capabilities, marking a paradigm shift from task-specific AI systems. This is why FMs are the go-to solution and are getting more and more attention over the last years. However, their adoption faces critical challenges such as i) dependence on centralized cloud infrastructures and providers, which in turn threatens digital sovereignty and data security; ii) high energy demands of running large-scale FMs that conflict with sustainability goals; iii) prohibitive costs risk excluding small and medium enterprises (SMEs) from deploying and customizing FMs tailored to their needs; iv) and reliance on remote services introducing latency and single points of failure. To address these challenges, the f-inference project envisions a Resource-driven Computing Continuum (RCC) that enables the hyper-distribution of FMs and their deployment across Cloud, Edge, and IoT tiers. In the proposed architecture, FM inference is pushed closer to where the data resides and end-users through optimization strategies, and adaptive and efficient inference methodologies, thus reducing latency, energy consumption, and operational costs of self-deploying FMs while preserving data sovereignty at the same time. To further demonstrate and validate the proposed methodologies, the f-inference project has employed partners in diverse domains, including Situational Awareness, Resource Management, and Advanced Industrial Systems. By democratizing access to advanced GenAI and evaluating the applicability of the proposed solutions in diverse environments, the f-inference project empowers SMEs and resource-constrained communities, increasing competitiveness and paving the way for equitable digital transformation.

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL4-2025-04

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Coordinator

EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTON
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 636 250,00
Address
PATISION 42
106 82 ATHINA
Greece

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Region
Αττική Aττική Κεντρικός Τομέας Αθηνών
Activity type
Research Organisations
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 636 250,00

Participants (20)