NFS at Scale for AI Workloads
Scale Network File System for AI training and inference using managed cloud services, pNFS parallelism, and hybrid tiering strategies.
NFS for AI: Practical Reality
NFS is the simplest shared file system to deploy and is universally supported. While it cannot match the raw throughput of Lustre or BeeGFS, modern managed NFS services have significantly improved performance and can serve many AI workloads, especially inference, model serving, and smaller-scale training.
Cloud-Managed NFS Options
| Service | Max Throughput | Max IOPS | Best For |
|---|---|---|---|
| Amazon EFS | 20+ GB/s (elastic) | 500K+ (elastic) | Kubernetes AI workloads |
| Google Filestore | 16 GB/s (Enterprise) | 480K | Vertex AI, GKE ML |
| Azure NetApp Files | 4.5 GB/s per volume | 450K | Azure ML, HPC |
| AWS FSx for NetApp ONTAP | 4 GB/s | 160K | Multi-protocol AI environments |
Optimizing NFS for ML Data Loading
# Optimized NFS mount for AI training data reads mount -t nfs4 -o \ nfsvers=4.1,\ rsize=1048576,\ wsize=1048576,\ hard,\ timeo=600,\ retrans=2,\ noresvport,\ async \ fs-12345.efs.us-east-1.amazonaws.com:/ /mnt/training-data # Key options explained: # rsize/wsize=1MB: Maximum read/write block size # hard: Retry indefinitely (prevents training job crashes) # async: Allows buffered writes for checkpoint performance
pNFS: Parallel NFS
Parallel NFS (pNFS) extends NFS 4.1 to support parallel data access. The metadata server provides layout information so clients can read data directly from multiple storage devices simultaneously, similar to how Lustre striping works.
When to Choose NFS vs Parallel File Systems
Choose NFS When
Workloads need less than 10 GB/s aggregate throughput, teams want zero operational overhead with managed services, or inference serving needs simple shared model storage.
Avoid NFS When
Training jobs need more than 20 GB/s throughput, workloads involve millions of small files, or checkpoint writes from hundreds of processes create metadata storms.