Edge intelligence unleashed: a survey on deploying large language models in resource-constrained environments
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Abstract
Edge computing environments face unprecedented challenges in deploying large language models due to severe resource constraints, latency requirements, and privacy concerns that traditional cloud-based solutions cannot address. Current approaches struggle with the fundamental mismatch between LLMs' computational demands - requiring gigabytes of memory and billions of operations - and edge devices' limited capabilities, resulting in either degraded performance or infeasible deployments. This survey presents a systematic analysis of emerging techniques that enable efficient LLM deployment at the edge through four complementary strategies: model compression via quantisation and pruning that reduces memory footprint by up to 75% while maintaining accuracy, knowledge distillation frameworks achieving 4000× parameter reduction with comparable performance, edge-cloud collaborative architectures like EdgeShard delivering 50% latency reduction through intelligent workload distribution, and hardware-specific optimisations leveraging specialised accelerators. Extensive evaluation across multiple real-world testbeds demonstrates that hybrid edge-microservices architectures achieve 46% lower P99 latency and 67% higher throughput compared to monolithic approaches, while supporting 10,000 concurrent users with 100 ms latency constraints and reducing bandwidth consumption by 99.5% through selective cloud offloading. These advancements enable transformative applications in healthcare monitoring, autonomous systems, real-time IoT analytics, and personalised AI services, fundamentally reshaping how intelligence is delivered at the network edge while preserving privacy and ensuring responsiveness critical for next-generation computing paradigms.
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Accepted 2025-10-10
Published 2025-10-10
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