Blog
Inferless solved critical I/O bottlenecks in LLM inference infrastructure by implementing Alluxio, achieving 10x faster model loading (from ~200 Mbps to 2+ Gbps), reducing cold start times from minutes to seconds, and significantly improving customer experience.
Coupang, a Fortune 200 technology company, manages a multi-cluster GPU architecture for their AI/ML model training. This architecture introduced significant challenges, including:
- Time-consuming data preparation and data copy/movement
- Difficulty utilizing GPU resources efficiently
- High and growing storage costs
- Excessive operational overhead maintaining storage for localized data silos
To resolve these challenges, Coupang’s AI platform team implemented a distributed caching system that automatically retrieves training data from their central data lake, improves data loading performance, unifies access paths for model developers, automates data lifecycle management, and extends easily across Kubernetes environments. The new distributed caching architecture has improved model training speed, reduced storage costs, increased GPU utilization across clusters, lowered operational overhead, enabled training workload portability, and delivered 40% better I/O performance compared to parallel file systems.
.png)
.jpeg)
This is part 2 of the blog series talking about the design and implementation of the Cross Cluster Synchronization mechanism in Alluxio. In the previous blog, we discussed the scenario, background and how metadata sync is done with a single Alluxio cluster. This blog will describe how metadata sync is built upon to provide metadata consistency in a multi-cluster scenario.
This is a blog series talking about the design and implementation of the Cross Cluster Synchronization mechanism in Alluxio. This mechanism ensures that the metadata is consistent when running multiple Alluxio clusters. Part 1 of this blog series discusses the scenario and background.
.jpeg)
.jpeg)
.jpeg)
.jpeg)
Modern analytics projects rely on a hodgepodge of compute clusters, data stores, and pipelines, flung across countries and continents. Enterprises struggle to meet performance SLAs without replicating lots of data or moving and re-coding applications.
.jpeg)