AI workloads have diverse storage requirements that cannot be met by a single metric. Training, inference, model distribution, agent, and data lake scenarios each demand different balances of throughput, latency, concurrent access, POSIX compatibility, consistency, cost, and operational complexity. This article provides a structured comparison of three prominent storage systems: GPFS (IBM's parallel file system), Alluxio (a distributed cache layer), and JuiceFS (a POSIX-compatible distributed file system built on object storage). It examines how each system handles the specific challenges of AI pipelines, such as high-throughput data loading for training, low-latency access for inference, and efficient model versioning. The analysis emphasizes that storage selection should be driven by the specific workload characteristics rather than generic performance claims. For AI infrastructure teams, this guide offers a clear framework for evaluating storage options in the context of real-world deployment scenarios.
A practical comparison of GPFS, Alluxio, and JuiceFS for AI workloads, covering training, inference, model distribution, and data lakes with trade-off analysis.