This article is part of our comprehensive guide to Kubernetes storage for AI workloads, where we explore the full landscape of storage challenges and solutions for AI/ML workloads.
You’re looking at enterprise storage platforms for your Kubernetes AI workloads. Nutanix NDK, Portworx, JuiceFS, Pure FlashArray, OpenShift Data Foundation, Dell PowerStore—they all promise advanced data services and AI-optimised performance. But how do you cut through the marketing?
MLPerf Storage v2.0 provides an objective benchmark framework comparing vendor performance on real AI training workloads including ResNet-50, 3D U-Net, and CosmoFlow. The metrics that matter: GPU utilisation thresholds of 90% for ResNet and U-Net or 70% for CosmoFlow, bandwidth utilisation efficiency, supported accelerator scale, and 3-5 year total cost of ownership.
This framework helps you make evidence-based vendor selections balancing performance requirements against budget constraints and multi-cloud portability needs.
What is MLPerf Storage v2.0 and Why Does It Matter for Enterprise Kubernetes Storage?
MLPerf is a universal AI benchmark suite from MLCommons that evaluates storage performance through real AI training scenarios. Unlike traditional benchmarks measuring IOPS or throughput in isolation, MLPerf Storage tests real-world machine learning with actual training datasets and access patterns.
The benchmark uses three workloads. ResNet-50 tests random reads of small 150 KB samples—high IOPS demand. 3D U-Net tests sequential reads of large 3D medical images—evaluating throughput. CosmoFlow tests concurrent small-file access requiring aggregate throughput and metadata stability.
MLPerf v2.0 requires vendors to meet GPU utilisation thresholds—90% for 3D U-Net and ResNet-50, 70% for CosmoFlow. These prove storage feeds GPUs fast enough to avoid idle compute. The primary metric is maximum supported accelerator count, not raw bandwidth claims.
Vendors must publish reproducible configurations. No more marketing without proof.
How Does the CSI Standard Limit Enterprise Kubernetes Storage Capabilities?
The Container Storage Interface acts like a storage integration contract for Kubernetes. Kubernetes doesn’t ship an opinionated storage stack but relies on CSI to plug into whatever layer you choose.
Standard CSI lacks disaster recovery, cross-cluster replication, application-aware snapshots, encryption, and backup integration. Performance features like GPU Direct Storage, NVMe over RDMA, and intelligent caching require vendor-specific extensions.
Going beyond basic provisioning requires vendor-specific customisation which introduces problems. Each vendor’s CSI driver has its own learning curve. CSI sprawl complicates upgrades and migrations. CSI isn’t designed for Kubernetes’ dynamic, distributed scheduling.
Multi-cloud portability requires a storage platform layer above CSI. You need one single interface not a plague of drivers, self-service automation not manual configuration, unified storage across environments.
Which Kubernetes Storage Vendors Lead MLPerf Storage v2.0 Benchmark Results?
JuiceFS achieved 108 GiB/s on 3D U-Net supporting 40 H100 GPUs with 86.6% bandwidth utilisation and 92.7% GPU utilisation—highest among Ethernet systems. For CosmoFlow, JuiceFS supported 100 H100 GPUs with 75% GPU utilisation. On ResNet-50, 500 H100 GPUs with 95% GPU utilisation.
Nutanix Unified Storage served 2,312 accelerators on ResNet-50. Per-node performance doubled from 71 to 144 for A100 GPUs, 35 to 71 for H100s.
Lightbits achieved 51% improvement for A100 and 16% for H100 on ResNet-50 using three commodity storage servers.
InfiniBand vendors like DDN, Hewlett Packard, and Ubix deliver high bandwidth appliances. InfiniBand excels at CosmoFlow with low, stable latency.
Dell PowerStore and Pure FlashArray lack MLPerf results but provide hybrid cloud validation through AWS Outposts and cloud-first architecture.
What Are the Key Architectural Differences Between Kubernetes Storage Vendors?
InfiniBand vendors like DDN, Hewlett Packard, and Ubix offer appliances delivering 400 GiB/s to 1,500 GiB/s. InfiniBand provides extreme bandwidth through specialised networking but requires infrastructure investment. High bandwidth utilisation gets harder as speeds increase—hitting 80% with 400 Gb/s+ NICs is difficult.
Ethernet systems like JuiceFS run on commodity hardware with standard networks—operational simplicity and lower entry barriers. Some vendors like Nutanix use RoCE-based Ethernet for higher bandwidth.
JuiceFS uses three layers: client nodes, cache cluster, and Google Cloud Storage backend. Before training, data warms from GCS to cache. During training, clients read from cache avoiding high-latency object storage. JuiceFS reaches 1.2 TB/s bandwidth after scaling cache nodes. Training scale doubles? Scale cache proportionally.
Nutanix Unified Storage offers file, object, and block storage from one platform using NFS without proprietary hardware. Converged platforms integrate compute, storage, and networking with GPU Direct Storage.
Lightbits separates storage from compute, unleashing NVMe drives for low latency and high throughput. Start minimal, scale by adding commodity hardware.
Portworx and Pure FlashArray prioritise multi-cloud portability, running identically on EKS, GKE, AKS, or on-premises OpenShift.
How Should CTOs Calculate Total Cost of Ownership for Kubernetes Storage Platforms?
Evaluate 3-5 year lifecycle costs: hardware, software licensing, cloud consumption, operational overhead, and scaling. Cloud CSI drivers like EBS appear cheaper initially but costs spiral as data volumes and GPU clusters grow from POC to production.
Think in phases. Phase 1 (months 0-6): speed and experimentation. Phase 2 (months 6-18): cost efficiency with production shifting on-premises for 50-70% savings. Phase 3 (18+ months): core AI on dedicated infrastructure, cloud for experimentation.
On-premises platforms require upfront investment but provide predictable costs and higher bandwidth utilisation. Hybrid approaches like Dell PowerStore on AWS Outposts balance cloud flexibility with on-premises economics. For deeper cost analysis and optimisation tactics, see our guide on enterprise vendor TCO analysis covering per-experiment cost attribution and orphaned PVC detection.
MLPerf’s bandwidth utilisation metric indicates software efficiency and infrastructure ROI. Higher utilisation means better cost-effectiveness. Lower utilisation? You’re paying for network capacity your storage can’t use.
Include hidden costs: egress charges, inefficient bandwidth utilisation, migration risks, operational overhead of cache warm-up or complex replication.
What Questions Should CTOs Ask During a Kubernetes Storage Vendor RFP Process?
Request published MLPerf Storage v2.0 results for ResNet-50, 3D U-Net, and CosmoFlow with accelerator counts and GPU utilisation percentages. Vendors should provide reproducible configurations showing exactly what hardware achieved results. Red flag: vague claims without benchmark data.
Require architecture documentation explaining GPU Direct Storage support, RDMA capabilities, and distributed cache implementations. Deep-dive questions: NVMe over RDMA versus TCP trade-offs, erasure coding versus replication, metadata handling at scale. Green flag: architecture diagrams with specific technical justifications.
Ask for customer references with similar scale—GPU count, dataset sizes, deployment models. Contact them directly. Ask about production performance, migration ease, operational complexity, whether claims matched reality.
Demand TCO calculators or reference pricing for 50-500 GPU deployments including licensing, support, and upgrades. List prices hide annual maintenance, professional services, and training. Ask about costs at current scale, 2x scale, and 5x scale.
Clarify multi-cloud portability, migration paths from CSI drivers, and StatefulSet migration with downtime expectations. Portworx provides one-click migration moving entire AI stacks between cloud and on-premises. For practical implementation details, consult our configuring Portworx storage classes guide covering YAML examples and acceptance criteria.
Request documentation:
- Architecture diagrams with data flow and failure scenarios
- Customer deployment test results not lab benchmarks
- Case studies with specific metrics
- Disaster recovery runbooks
MLPerf submission requirements show what detail serious vendors provide.
How Do Nutanix NDK, Portworx, and JuiceFS Compare for AI Training Workloads?
Nutanix NDK excels at scale—2,312 accelerators on ResNet-50 through converged infrastructure using GPU Direct Storage and SR-IOV. Uses standard NFS without proprietary hardware. File, object, and block storage from one system.
Nutanix requires converged infrastructure where compute and storage deploy together—higher upfront costs but simpler operations and predictable performance. Suits organisations planning significant on-premises AI investment.
JuiceFS leads bandwidth utilisation at 86.6% for 3D U-Net via distributed cache separating hot data from Google Cloud Storage. Provides 1.2 TB/s bandwidth through elastic scaling. Training scale doubles? Scale cache proportionally.
JuiceFS leverages existing Kubernetes clusters plus object storage, avoiding specialised hardware. Delivers cost-effective cloud-native storage but requires understanding cache warm-up. Works for organisations with cloud presence wanting high-performance access to cloud-stored data.
Portworx prioritises multi-cloud portability and enterprise data services—disaster recovery, backup, replication. Works identically on EKS, GKE, AKS, or on-premises OpenShift providing true multi-cloud flexibility. Integrates with Pure FlashArray as backing storage.
Portworx runs on commodity hardware as software-only deployment. Data services layer provides snapshots, encryption, and replication which CSI drivers lack. Suits multi-cloud strategies accepting performance meeting requirements without leading benchmarks.
Cost-performance positioning: Nutanix at scale and performance end, JuiceFS at cost-effective cloud-native position, Portworx at enterprise features and flexibility. Choose based on priorities—maximum scale (Nutanix), bandwidth efficiency (JuiceFS), or portability (Portworx). If you’re also evaluating cloud provider storage comparison, consider hybrid approaches combining enterprise vendors with cloud services.
What About OpenShift Data Foundation, Pure FlashArray, and Dell PowerStore?
OpenShift Data Foundation (formerly Red Hat Ceph) provides software-defined storage integrated with Red Hat OpenShift. Suits Red Hat shops wanting storage integrated with OpenShift. Provides file, block, and object storage through Ceph but lacks MLPerf results.
Pure FlashArray integrates with Portworx for cloud-first AI scaling leveraging Pure’s flash arrays as backing storage. Combines Pure’s data reduction with Portworx’s multi-cloud data services. Suits Pure Storage customers extending to Kubernetes AI workloads. Lacks standalone MLPerf results.
Dell PowerStore validated for AWS Outposts hybrid cloud deployments but lacks MLPerf results. Provides 5:1 data reduction through inline deduplication and compression. Suits hybrid strategies with some workloads on AWS, sensitive data on-premises. Without MLPerf validation, requires vendor POC testing.
These target specific deployment models not MLPerf competition. OpenShift Data Foundation for Red Hat shops, Pure FlashArray for existing customers, Dell PowerStore for hybrid AWS. Weigh ecosystem integration against performance transparency from MLPerf-validated vendors. For guidance on vendor BCDR capabilities comparison, evaluate how Nutanix NDK and Portworx data protection features align with your compliance requirements.
How Do Performance Technologies Like GPU Direct Storage and NVMe over RDMA Impact Real-World AI Workloads?
GPU Direct Storage creates a direct path between storage and GPU memory bypassing CPU, improving throughput and reducing latency. Provides 15-25% performance improvement. Nutanix NDK implements GPU Direct contributing to 2,312 accelerator support on ResNet-50.
Without GPU Direct, data flows from storage to system memory, then CPU copies to GPU. GPU Direct eliminates CPU involvement, freeing CPU resources.
NVMe over RDMA reduces network stack latency. InfiniBand networks excelled in CosmoFlow with low, stable latency. RDMA protocols eliminate kernel involvement achieving sub-microsecond latencies.
InfiniBand provides higher bandwidth and lower latency than Ethernet but requires specialised infrastructure. RoCE (RDMA over Converged Ethernet) offers middle ground combining RDMA with standard Ethernet. Lower latency than TCP/IP while avoiding InfiniBand costs. Requires lossless Ethernet with Priority Flow Control.
NVMe over TCP uses standard TCP/IP without RDMA. Lightbits uses NVMe over TCP achieving strong performance on commodity Ethernet. Sacrifices some latency for operational simplicity.
Evaluate workload latency sensitivity. CosmoFlow with many small files benefits from RDMA and InfiniBand. ResNet-50 and 3D U-Net may not justify specialised networking if software achieves high bandwidth utilisation on Ethernet.
Consider team expertise. InfiniBand and RoCE require skills Ethernet teams may lack. Factor training and operational complexity into TCO.
FAQ Section
What is the difference between CSI drivers and enterprise Kubernetes storage platforms?
CSI drivers provide basic volume provisioning API while enterprise platforms add data services including disaster recovery, replication, snapshots, and encryption plus performance optimisations like GPU Direct Storage and RDMA. Platforms justify investment when AI workloads require advanced features or exceed CSI performance limitations.
How many accelerators should a Kubernetes storage platform support for production AI training?
Production requirements vary but MLPerf benchmarks show leading vendors supporting 100-2,312 accelerators. SMB deployments typically start at 10-50 GPUs and scale to 100-200. Your storage platform should support 2-3x current GPU count for growth headroom.
Which MLPerf Storage workload best represents AI training performance?
Depends on your AI application. ResNet-50 for image classification with high IOPS and small files, 3D U-Net for medical imaging and segmentation requiring sequential throughput, or CosmoFlow for scientific computing needing low latency. Evaluate vendors on the workload matching your use case.
Is cloud-managed storage like EBS sufficient for Kubernetes AI workloads?
EBS provides convenience but bandwidth limitations and costs escalate at scale. Suitable for POC with 10-20 GPUs but production workloads with 100+ GPUs typically require enterprise platforms for performance and TCO optimisation.
How long does migration from CSI drivers to enterprise storage platforms take?
Depends on StatefulSet count and downtime tolerance. Vendors like Portworx offer migration tools but expect 2-4 weeks for planning, testing, and execution with 50-200 StatefulSets. Zero-downtime migrations require careful orchestration.
What bandwidth utilisation percentage indicates efficient storage software?
MLPerf Storage v2.0 results show top performers achieving 80-87% bandwidth utilisation with JuiceFS leading at 86.6%. Below 60% suggests software inefficiency wasting network infrastructure investment. Target 70%+ for production deployments.
Should we prioritise on-premises or cloud deployment for AI storage?
Hybrid approaches are often optimal with cloud for development and POC providing flexibility and quick starts, while on-premises handles production providing predictable costs and high bandwidth. Dell PowerStore on AWS Outposts and Portworx multi-cloud enable gradual migration.
How important is GPU Direct Storage for AI training workloads?
Provides 15-25% performance improvement by removing CPU from the data path. Important for large-scale training with 200+ GPUs where efficiency gains compound. Nutanix NDK implements this.
What is distributed cache architecture and when does it make sense?
Separates hot frequently-accessed data in a cache tier from cold archival storage in object storage. JuiceFS uses this approach for cost-effective terabyte-scale bandwidth, reducing storage costs 40-60% versus all-flash but requiring a warm-up process.
Can we avoid vendor lock-in with Kubernetes storage platforms?
Portworx and other cloud-first platforms prioritise multi-cloud portability. Key step: validate workload migration capabilities between AWS, Azure, GCP, and on-premises during your RFP. Test actual migration not just vendor claims.
What TCO factors beyond licensing costs should we consider?
Network infrastructure costs (InfiniBand versus Ethernet), cloud egress charges, operational overhead from distributed cache warm-up complexity, bandwidth utilisation efficiency where lower percentages mean wasted network spending, and scaling trajectory costs over 3-5 years.
How do we benchmark our specific AI workload instead of relying on MLPerf?
MLPerf provides standardised comparison baseline. For workload-specific validation, request vendor POC with your actual training data, model architectures, and GPU configurations. Measure GPU utilisation, training time, and cost per experiment.
For the complete landscape of Kubernetes storage challenges and solutions for AI workloads, see our complete vendor landscape guide covering CSI limitations, performance requirements, cloud providers, implementation guides, BCDR strategies, and cost optimisation.
