# EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training

arXiv: https://arxiv.org/abs/2508.11035 · HTML: https://mlsanity.com/paper/2508.11035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2025.

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

- [From FLOPs to Footprints: The Resource Cost of Artificial Intelligence](https://mlsanity.com/paper/2512.04142/) (2025)

## Abstract (arXiv)

Large-scale deep learning workloads increasingly suffer from I/O bottlenecks as datasets grow beyond local storage capacities and GPU compute outpaces network and disk latencies. While recent systems optimize data-loading time, they overlook the energy cost of I/O - a critical factor at large scale. We introduce EMLIO, an Efficient Machine Learning I/O service that jointly minimizes end-to-end data-loading latency T and I/O energy consumption E across variable-latency networked storage. EMLIO deploys a lightweight data-serving daemon on storage nodes that serializes and batches raw samples, streams them over TCP with out-of-order prefetching, and integrates seamlessly with GPU-accelerated (NVIDIA DALI) preprocessing on the client side. In exhaustive evaluations over local disk, LAN (0.05 ms & 10 ms RTT), and WAN (30 ms RTT) environments, EMLIO delivers up to 8.6X faster I/O and 10.9X lower energy use compared to state-of-the-art loaders, while maintaining constant performance and energy profiles irrespective of network distance. EMLIO's service-based architecture offers a scalable blueprint for energy-aware I/O in next-generation AI clouds.