MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce near-real-time responses and exhibit broad hardware compatibility, maximizing user reach. We present a methodology for designing such models using hardware-in-the-loop architecture search under mobile latency constraints. This system is amenable to industry-scale deployment: it generates models deployable without custom kernels and compatible with standard mobile runtimes like Executorch. Our methodology avoids specialized attention mechanisms and instead uses attention skipping for long-context acceleration. Our a...
| Authors | Hanxian Huang, Igor Fedorov, Andrey Gromov et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 March 2026 |
| Last updated at source | 27 April 2026 |
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