Hibernation of Tensor Computations in Artificial Intelligence Systems

This document introduces the architectural principle of tensor computation hibernation for large language model (LLM) inference systems. The core innovation enables suspending neural network execution at arbitrary points, serializing the complete computational state (hidden states, key-value cache, positional encodings), and resuming computation on a different model or GPU without reverting to text representation. This eliminates mandatory detokenization boundaries between processing stages, enabling modular inference pipelines where specialized models exchange frozen latent states rather than tokens. The approach addresses fundamental limitations in distributed LLM inference: linear VRAM scaling, high latency from repeated tokenization cycles, and inability to transfer intermediate computational states. Applications include multi-agent systems, long-context processing across heterogeneous models, memory-disaggregated architectures, and speculative execution with state branching. The document establishes prior art for this architectural pattern and presents a reference implementation framework. Keywords: LLM inference optimization, resumable neural network state, distributed tensor computation, GPU memory efficiency, modular language model architectures, cross-model state transfer, activation checkpoint protocol