arXiv:2606.03770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themselves, practical deployment must address cost efficiency, low latency, and optimal resource utilization. Conventional approaches typically assume that an entire model can be hosted on a single device, which does not hold in many real-world scenarios, particularly in Edge and Fog environments where device resources are constrained. In this paper, we introduce E2LLM, a framework designed to enable ef
The proliferation of LLMs and the increasing demand for real-time AI inference at the edge necessitate solutions for efficient deployment on resource-constrained devices, a challenge that current methods often fail to address.
This development is crucial for expanding the practical applicability of LLMs beyond datacenters, enabling new use cases in environments where latency, cost, and resource limitations are critical factors.
The ability to efficiently deploy and serve large language models across heterogeneous edge and fog computing environments becomes more viable, moving away from the assumption of single-device hosting.
- · Edge AI hardware manufacturers
- · Developers of edge-native AI applications
- · Telecommunication companies (fog computing)
- · Companies requiring on-device, low-latency AI
- · Cloud-centric LLM providers for specific use cases
- · Companies relying solely on high-power, centralized AI inference
- · Developers ignoring edge constraints
Wider adoption and commercialization of LLMs in embedded systems and IoT devices will accelerate.
Increased demand for specialized edge AI accelerators and optimized model architectures will emerge.
This could lead to new business models built around distributed, low-latency AI services, potentially impacting data sovereignty and privacy discussions due to localized processing.
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Read at arXiv cs.AI