Compressing An 11B VLM To 2.7-bit Weights For Mobile CPUs

As multimodal AI moves from the cloud to personal devices, developers face a fundamental constraint. Capable vision-language models require far more memory and bandwidth than most mobile systems can provide. Mobile phones are a natural place to run multimodal AI. They already have cameras, microphones, sensors, and personal context on a single device. Running models locally also reduces reliance on cloud services, helping keep personal data on-device while reducing latency and improving responsi