AI-Generated Images: The Memory Problem with LLMs on Robots

Artificial intelligence (AI) and robotics have made tremendous progress in recent years, but one of the biggest challenges facing developers is how to integrate large language models (LLMs) into real-time applications. One such application is vision-language-action robots that can perceive their environment, understand commands, and take action accordingly. However, when a normal chat-oriented LLM runtime is pointed at a live robot camera feed, three things break almost simultaneously: VRAM overflows from the continuous stream of visual tokens, control-loop deadlines get missed silently, and the reasoning step falls behind the 60Hz camera’s pace.