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SLA-Aware Distributed LLM Inference Across Device-RAN-Cloud
arXiv – CS AI|Hariz Yet, Nguyen Thanh Tam, Mao V. Ngo, Lim Yi Shen, Lin Wei, Jihong Park, Binbin Chen, Tony Q. S. Quek||3 views
🤖AI Summary
Researchers tested distributed AI inference across device, edge, and cloud tiers in a 5G network, finding that sub-second AI response times required for embodied AI are challenging to achieve. On-device execution took multiple seconds, while RAN-edge deployment with quantized models could meet 0.5-second deadlines, and cloud deployment achieved 100% success for 1-second deadlines.
Key Takeaways
- →On-device AI inference fails to meet sub-second requirements for embodied AI applications in 5G networks
- →RAN-edge deployment can achieve sub-0.5 second response times but only with quantized AI models
- →Cloud-based inference meets 1-second deadlines consistently but struggles with 0.5-second requirements over WAN
- →Multi-Instance GPU isolation successfully preserves baseband processing health under concurrent AI workloads
- →Model quantization is critical for meeting strict latency requirements in edge AI deployments
Read Original →via arXiv – CS AI
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