arXiv:2606.23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck. Existing systems primarily optimize for decoding speed, implicitly assuming that faster execution is always preferable. We show instead that on-device LLM inference often has exploitable configuration slack: modestly lowering NPU and memory frequencies preserves quality of experience (QoE) while substantially improving energy efficiency and reducing heat. Realizing thi
The proliferation of LLMs creates a pressing need to make them practical for on-device deployment, where energy constraints are paramount, pushing research into efficiency optimizations beyond raw speed.
This research provides a pathway to make sophisticated AI more accessible, private, and environmentally sustainable by addressing a critical bottleneck in its widespread deployment.
The focus for on-device LLM optimization expands from decoding speed to a more holistic energy-efficiency approach, potentially altering hardware design and software deployment strategies.
- · Edge AI hardware manufacturers
- · On-device LLM developers
- · Consumer electronics industry
- · Energy-efficient AI startups
- · Cloud-dependent LLM providers (to a small degree)
- · Hardware vendors prioritizing raw speed over efficiency
On-device LLMs become more viable for a wider range of applications and lower-power devices.
Increased adoption of localized AI reduces reliance on centralized cloud infrastructure, improving privacy and reducing data transmission costs.
A shift in competitive advantage towards developers and hardware manufacturers that can deliver high-performance, energy-efficient AI at the edge.
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Read at arXiv cs.LG