ConsciousLLM: The Future of Intelligent Deployment of Self-aware Models

As the complexity of large language models (LLMs) increases, so does their parameter count and size. While LLMs with a substantial number of parameters yield highly accurate results, their deployment presents significant challenges even for enterprises. Existing methods for distributing transformer blocks across multiple nodes for inference are well-known; however, the responsibility for distribution typically rests with the user, often resulting in sub-optimal resource utilization.