TensorDex: A Compact, Tensor-Centric Storage System for Modern AI Models

Modern model hubs store hundreds of petabytes of large language models (LLMs), with fine-tuned variants dominating the storage footprint. These variants contain substantial cross-model redundancy that delta compression can exploit by storing only the difference between a target and a reference model. However, compression effectiveness depends critically on choosing a similar reference. At model-hub scale, this is challenging because model lineage metadata is often missing or unreliable, and different tensors within the same model may be most similar to tensors from different models. Consequently, model-level pairing leaves substantial redundancy unexploited.