# annbatch unlocks terabyte-scale training of biological data in anndata

arXiv: https://arxiv.org/abs/2604.01949 · HTML: https://mlsanity.com/paper/2604.01949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery](https://mlsanity.com/paper/2411.10548/) (2024)
- [scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics](https://mlsanity.com/paper/2506.01883/) (2025)
- [Same model, better performance: the impact of shuffling on DNA Language Models benchmarking](https://mlsanity.com/paper/2510.12617/) (2025)

## Abstract (arXiv)

The scale of biological datasets now routinely exceeds system memory, making data access rather than model computation the primary bottleneck in training machine-learning models. This bottleneck is particularly acute in biology, where widely used community data formats must support heterogeneous metadata, sparse and dense assays, and downstream analysis within established computational ecosystems. Here we present annbatch, a mini-batch loader native to anndata that enables out-of-core training directly on disk-backed datasets. Across single-cell transcriptomics, microscopy and whole-genome sequencing benchmarks, annbatch increases loading throughput by up to an order of magnitude and shortens training from days to hours, while remaining fully compatible with the scverse ecosystem. Annbatch establishes a practical data-loading infrastructure for scalable biological AI, allowing increasingly large and diverse datasets to be used without abandoning standard biological data formats. Github: https://github.com/scverse/annbatch