KIM, D.; Jeong, K.; KIM, K.

Foundation models (FMs) pre-trained on large-scale single-cell RNA sequencing (scRNA-seq) data provide powerful cell embeddings, but their practical usability and systematic comparison are limited by model-specific environments, preprocessing pipelines, and execution procedures. To address these challenges, we introduce scUnify, a unified zero-shot inference framework for single-cell foundation models. scUnify accepts a standard AnnData object and automatically manages environment isolation, preprocessing, and tokenization through a registry-based modular design. It employs a hierarchical distributed inference strategy that combines Ray-based task scheduling with multi-GPU data-parallel execution via HuggingFace Accelerate, enabling scalable inference on datasets containing up to one million cells. In addition, built-in integration of scIB and scGraph metrics enables standardized cross-model embedding evaluation within a single workflow. Benchmarking results demonstrate substantial reductions in inference time compared with the original model implementations, while preserving embedding quality and achieving near-linear multi-GPU scaling. scUnify is implemented in Python and is publicly available at https://github.com/DHKim327/scUnify.