Micro Language Models Enable Instant Responses
Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute constraints, yet cloud inference introduces multi-second latencies that break the illusion of a responsive assistant. We introduce micro language models ($μ$LMs): ultra-compact models (8M-30M parameters) that instantly generate the first 4-8 words of a contextually grounded response on-device, while a cloud model completes it; thus, masking the cloud latency. We show that useful language generation survives at this extreme scale with our models matching several 70M-256M-class existing models. We design a collaborative generation framework...
| Authors | Wen Cheng, Tuochao Chen, Karim Helwani et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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