We're thrilled to share that Oracle Cloud Infrastructure has published a technical solution blog demonstrating how Alluxio on Oracle Cloud Infrastructure (OCI) delivers exceptional performance for AI and machine learning workloads. This collaboration between engineering teams showcases the power of combining Alluxio's data acceleration layer with OCI's high-performance bare-metal infrastructure, achieving sub-millisecond average latency, near-linear scalability, and over 90% GPU utilization across 350 accelerators.
The benchmark results speak for themselves: Alluxio on OCI achieved 0.3ms average latency for single-node deployments in the WARP benchmark and scaled to 61.6 GB/s throughput across six nodes while maintaining GPU utilization above 90% in MLPerf Storage 2.0 testing.
What makes this particularly exciting is the flexibility it offers customers. Whether deploying in dedicated mode for maximum performance or co-located mode for cost efficiency, OCI customers eliminate data access bottlenecks with Alluxio - all without migrating data or changing application code. This is exactly the kind of plug-and-play integration that accelerates time-to-value for AI infrastructure.
The combination of Alluxio and OCI addresses one of the most critical challenges in AI infrastructure today: keeping expensive GPU resources fully utilized. By creating a high-performance caching layer between compute and object storage, we're helping organizations maximize their cloud investments and accelerate model training cycles and inference workloads.
We're grateful to the Oracle team, including Xinghong He, Pinkesh Valdria, and the entire OCI GPU Storage team, for their collaboration on these benchmarks.
Read the full technical blog to explore the detailed results and learn how to deploy Alluxio on OCI for your AI workloads.
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Blog
If you’re building large-scale AI, you’re already multi-cloud by choice (to avoid lock-in) or by necessity (to access scarce GPU capacity). Teams frequently chase capacity bursts, “we need 1,000 GPUs for eight weeks,” across whichever regions or providers can deliver. What slows you down isn’t GPUs, it’s data. Simply accessing the data needed to train, deploy, and serve AI models at the speed and scale required – wherever AI workloads and GPUs are deployed – is in fact not simple at all. In this article, learn how Alluxio brings Simplicity, Speed, and Scale to Multi-GPU Cloud deployments.
Alluxio's strong Q2 featured Enterprise AI 3.7 launch with sub-millisecond latency (45× faster than S3 Standard), 50%+ customer growth including Salesforce and Geely, and MLPerf Storage v2.0 results showing 99%+ GPU utilization, positioning the company as a leader in maximizing AI infrastructure ROI.