Fully homomorphic encryption has long promised to let AI models run on encrypted data without exposing it, but the technique's heavy computational and memory costs have kept it out of practical use. A new arXiv paper introduces Cerium, a multi-GPU framework designed to bring encrypted inference to terabyte-scale workloads.
The abstract notes that FHE enables inference directly over encrypted queries, offering strong privacy guarantees. However, the overheads have limited deployment. Cerium is presented as a response to those bottlenecks, targeting the scale and performance needed for real-world use.
The paper is a single source, and the abstract is brief, so details on Cerium's internal architecture or benchmark results are not available from the summary. The significance lies in the claim that multi-GPU parallelism can be harnessed to make FHE-based inference feasible at terabyte scale.