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 \(\approx 5\%\) when features fit in memory.

Most real-world graphs are dynamic in nature, with continuous and rapid updates to the graph topology, and vertex and edge properties. Such frequent updates pose significant challenges for inferencing over Graph Neural Networks (GNNs). Current approaches that perform vertex-wise and layer-wise inferencing are impractical for dynamic graphs as they cause redundant computations, expand to large neighborhoods, and incur high communication costs for distributed setups, resulting in slow update propagation that often exceeds real-time latency requirements. This motivates the need for streaming GNN inference frameworks that are efficient and accurate over large, dynamic graphs. We propose Ripple, a framework that performs fast incremental updates of embeddings arising due to updates to the graph topology or vertex features. Ripple provides a generalized incremental programming model, leveraging the properties of the underlying aggregation functions employed by GNNs to efficiently propagate updates to the affected neighborhood and compute the exact new embeddings. Besides a single-machine design, we also extend this execution model to distributed inferencing, to support large graphs that do not fit in a single machine’s memory. Ripple on a single machine achieves up to ≈ 28000 updates/sec for sparse graphs like Arxiv and ≈1200 updates/sec for larger and denser graphs like Products, with latencies of 0.1ms–1s that are required for near-realtime applications. The distributed version of Ripple offers up to ≈30× better throughput over the baselines, due to 70× lower communication costs during updates.