# ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks

arXiv: https://arxiv.org/abs/2605.09402 · HTML: https://mlsanity.com/paper/2605.09402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2026.

## Built on by (most cited first, 1 of 1)

- [Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs](https://mlsanity.com/paper/2607.17374/) (2026)

## Abstract (arXiv)

Graph Neural Network (GNN) inference on billion-scale graphs is critical for domains like fintech and recommendation systems. Full-graph inference on these large graphs can be challenging due to high communication costs in distributed settings and high I/O costs in disk-backed Out-of-Core (OOC) settings. Existing OOC systems, operating across disk and memory, primarily focus on GNN training and perform poorly for full-graph inference due to massive read amplification, irregular I/O, and memory pressure. We present ATLAS, a disk-based GNN inference framework that enables efficient full-graph, layer-wise inference on graphs whose topologies, features and intermediate embeddings exceed the available memory on single machines. ATLAS replaces gather-based execution with a broadcast-based model that enables sequential, single-pass streaming reads of features and embeddings per layer. A tiered memory-disk hierarchy with minimum-pending-message eviction, graph reordering and a GPU-accelerated pipeline sustains high throughput within 128 GiB RAM and 2 TiB SSD. Across out-of-core graphs with up to 4B edges and 550 GiB features and multiple GNN architectures, ATLAS improves end-to-end inference time by ≈12--30× over State-of-the-Art (SOTA) OOC baselines on a single workstation, while remaining within ≈5% when features fit in memory.