Objective
Edge deployment of Large Language Models (LLMs) plays a vital role in ensuring low latency, reducing communication overhead, and enhancing privacy, bridging the gap unaddressed by cloud and on-device LLMs. However, edge LLMs face daunting challenges due to resource constraints and highly dynamic, heterogeneous environments. Notably, the Mixture-of-Experts (MoE) architecture, as seen in models like DeepSeek, has emerged as a promising solution for edge deployment. MoE enables sparse activation, dramatically lowering computational load and supporting collaborative, distributed deployment. This adaptability makes MoE-based LLMs well-suited for challenging edge scenarios. Still, several barriers persist. MoE-based LLMs typically have larger parameter sizes than dense models, requiring substantial cache memory, which strains edge resources. Additionally, frequent and voluminous inter-server data transfers, combined with limited bandwidth in edge networks compared to cloud data centers, form a critical performance bottleneck. The complexity is further compounded by the diverse and fluctuating demands of edge resources and applications, making collaborative resource allocation and efficient scheduling particularly difficult. To address these issues, advanced strategies are proposed in this project. Inter-server and intra-server collaborative deployment methods partition models based on expert activation paths and similarities, ensuring efficient distribution across edge servers and optimal expert scheduling within each server. Mixed-precision quantization enables dynamic adaptation of expert bit-widths, balancing resource constraints, application requirements, and expert popularity. Innovative token pruning and fusion mechanisms reduce data transfer frequency and volume, enhancing overall inference efficiency. This project establishes the theoretical foundations and practical methodologies for realizing high-performance and ubiquitous edge LLMs.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project has not yet been classified with EuroSciVoc.
Be the first one to suggest relevant scientific fields and help us improve our classification service
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
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.
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.
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-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
See all projects funded under this callCoordinator
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.
100 44 Stockholm
Sweden
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.