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Survey details techniques for deploying LLMs on edge devices

By PulseAugur Editorial · [1 sources] ·

A new survey paper published on arXiv details the latest techniques enabling the deployment of large language models (LLMs) on resource-constrained edge devices. The paper highlights that while LLMs offer significant AI capabilities, their substantial resource demands have historically limited their use on mobile and edge hardware. Recent advancements in software optimizations and specialized hardware are making edge deployment increasingly feasible, offering benefits such as lower latency, reduced costs, and enhanced personalization. AI

IMPACT Enables wider adoption of advanced AI capabilities on consumer devices by addressing resource constraints.

RANK_REASON The cluster contains a survey paper published on arXiv detailing techniques for LLM deployment. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details techniques for deploying LLMs on edge devices

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The cluster contains a survey paper published on arXiv detailing techniques for LLM deployment. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Rahul Maliakkal, Yashasvi Makin, Plawan Rath, Rohit Jain, Ankur Sadhoo ·

    Large Language Model Deployment on Resource-Constrained Edge Devices: A Practitioners Survey

    arXiv:2611.09075v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed many areas of artificial intelligence (AI), but their substantial resource requirements limit their deployment on mobile and edge devices. Although LLMs have demonstrated impressive ca…

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