MobileLLM is an optimized small language model designed for mobile devices, focusing on the creation of high-quality LLMs with less than one billion parameters to cater to practical mobile deployments. Contrary to traditional beliefs, this research emphasizes the importance of model architecture in small LLMs. Through deep and thin architecture, combined with embedding sharing and grouped query attention mechanisms, MobileLLM achieves significant accuracy improvements and introduces a block-level weight sharing method that does not increase model size nor incur high latency costs. Furthermore, the MobileLLM family demonstrates remarkable improvements in chat benchmarks compared to previous small models, and approaches the accuracy of LLaMA-v2 7B in API call tasks, showcasing the potential of small models in common device use cases.