DDN EXAScaler – Scalable Parallel File System Appliances for AI, HPC, and Data-Intensive Workloads

Description

In the era of artificial intelligence, high-performance computing, and data-intensive workloads, storage infrastructure has emerged as a critical bottleneck determining whether organizations can fully leverage their computational investments or remain constrained by data access limitations. Traditional storage architectures struggle to deliver the extreme throughput, massive concurrency, and low-latency performance required by modern GPU-accelerated AI training, large-scale HPC simulations, and real-time analytics pipelines processing petabytes of data. DDN EXAScaler addresses these challenges head-on, delivering a purpose-built parallel file system appliance that combines Lustre’s proven scalability with DDN’s proprietary enhancements, creating the world’s most advanced storage platform for data-intensive applications.

DDN EXAScaler represents the culmination of over a decade of innovation in parallel filesystem technology, integrating cutting-edge NVMe flash storage, intelligent data management capabilities, and GPU-aware optimizations into turnkey appliances that eliminate the complexity traditionally associated with high-performance storage deployments. As the storage platform exclusively used by NVIDIA for their internal GPU clusters and DGX SuperPOD installations, EXAScaler has proven its capabilities in the most demanding AI and HPC environments worldwide, consistently topping industry benchmarks including IO500 and MLPerf Storage while delivering unprecedented performance density that dramatically reduces total cost of ownership.

This comprehensive product overview examines DDN EXAScaler’s architectural foundation, advanced features, performance characteristics, deployment options, and strategic advantages that make it the storage solution of choice for organizations building next-generation AI factories, supercomputing facilities, and data-intensive research infrastructure.

Understanding DDN EXAScaler: Architecture and Core Technology

DDN EXAScaler is fundamentally a parallel file system appliance combining DDN’s high-performance storage hardware with an enhanced version of the Lustre filesystem software, creating an integrated solution that delivers enterprise-class capabilities without the complexity of building custom storage infrastructure from open-source components.

Lustre Foundation with DDN Enhancements

At its core, EXAScaler leverages Lustre, the open-source parallel distributed file system originally developed for supercomputing environments and now powering the majority of the world’s top HPC installations. Lustre’s architecture separates metadata operations from data operations, enabling massive scalability through parallel data access patterns where hundreds or thousands of clients can simultaneously read and write data to multiple storage targets without bottlenecks.

DDN’s EXAScaler implementation extends standard Lustre with proprietary enhancements unavailable in community Lustre or competitive offerings:

Stratagem Data Orchestration Engine: A sophisticated policy-based data management framework that provides comprehensive data lifecycle controls, automated tiering, and intelligent data placement across storage tiers. Unlike basic hierarchical storage management (HSM) solutions, Stratagem operates transparently without requiring application modifications or user intervention.

Hot Pools Technology: Automated data movement between high-performance NVMe flash and cost-effective capacity disk tiers based on access patterns and usage heat. This intelligent tiering ensures frequently accessed data resides on fast storage while cold data migrates to high-capacity tiers, optimizing both performance and economics without manual intervention.

Hot Nodes Capability: A unique caching mechanism that automatically stages data on local NVMe storage within GPU compute nodes, dramatically reducing IO latency by eliminating network round trips for frequently accessed training datasets. This GPU-aware optimization proves particularly valuable for AI workloads where dataset caching directly improves GPU utilization and training throughput.

Client-Side Data Compression: Proprietary compression technology that reduces data size during client-side writes without performance degradation, overcoming the throughput penalties associated with server-side compression. This feature enables organizations to achieve 2:1 to 5:1 compression ratios for AI datasets while maintaining full line-rate performance.

Enhanced Monitoring and Management: The EXAScaler Management Framework (EMF) provides centralized orchestration, health monitoring, and automated management capabilities that vastly simplify operations compared to managing raw Lustre deployments. Online upgrades, comprehensive alerting, and job-level performance analytics enable IT teams to maintain large-scale installations efficiently.

Hyperconverged Storage Platform Integration

EXAScaler appliances integrate DDN’s Storage Fusion Architecture (SFA) hardware platforms, delivering purpose-built storage nodes optimized for parallel file system workloads:

All-NVMe Configurations: The AI400X3 series provides pure NVMe flash storage delivering up to 140 GB/s sequential read and 110 GB/s sequential write performance with 4 million IOPS in a compact 2U form factor. This extreme performance density enables organizations to build petabyte-scale storage systems with minimal data center footprint and power consumption.

Hybrid Flash-Disk Architectures: The ES400X2 series combines NVMe flash for performance with high-capacity HDDs for cost-effective long-term storage, leveraging Hot Pools automation to transparently manage data placement. This architecture proves ideal for organizations managing diverse workloads where economics matter as much as performance.

Flexible Scale-Out Design: EXAScaler deployments scale from single appliances supporting departmental workloads through massive installations spanning hundreds of appliances delivering exabyte-scale capacity and terabytes-per-second aggregate throughput. This architectural flexibility enables organizations to start small and grow incrementally as requirements evolve.

Parallel Architecture for Extreme Performance

The fundamental architectural advantage of EXAScaler lies in its parallel design where data flows simultaneously across multiple independent paths:

Multiple Object Storage Targets (OSTs): Each EXAScaler appliance exports numerous OSTs (typically 8-16 per appliance) that clients access concurrently. File data is striped across multiple OSTs, enabling aggregate bandwidth that scales linearly as appliances are added to the filesystem.

Distributed Metadata Management: Metadata operations (file creation, deletion, permission changes) are handled by dedicated Metadata Targets (MDTs) separate from data paths, preventing metadata bottlenecks that plague traditional filesystems during operations involving millions of small files.

Shared-Nothing Scalability: Unlike scale-up architectures where performance concentrates in a single controller that becomes a bottleneck, EXAScaler’s scale-out approach distributes intelligence across all appliances, enabling near-linear performance scaling as the system grows.

This architectural foundation enables EXAScaler to deliver capabilities impossible with traditional storage systems: supporting thousands of concurrent clients, sustaining terabytes-per-second of aggregate throughput, managing filesystems containing billions of files, and maintaining consistent performance characteristics as deployments scale from terabytes to exabytes.

Key Features and Advanced Capabilities

DDN EXAScaler differentiates itself through a comprehensive feature set addressing the full spectrum of data-intensive workload requirements, from AI model training through scientific simulations to large-scale analytics pipelines.

Performance at Unprecedented Scale

Extreme Throughput: Individual AI400X3 appliances deliver up to 140 GB/s sequential read and 110 GB/s write bandwidth in just 2U of rack space, while large-scale deployments routinely exceed 1 TB/s aggregate throughput. This performance enables feeding hundreds of GPUs simultaneously without storage becoming a bottleneck.

Massive Concurrency: EXAScaler supports thousands of concurrent clients accessing the filesystem simultaneously, with performance scaling linearly as client count increases. This capability proves essential for multi-user AI development environments and HPC clusters where hundreds of jobs run concurrently.

Low-Latency Access: With Hot Nodes caching and optimized IO paths, EXAScaler delivers sub-millisecond latencies for cached data and single-digit millisecond latencies for remote access, ensuring applications remain compute-bound rather than IO-bound.

Exceptional Metadata Performance: DDN’s metadata optimizations enable millions of file operations per second, critical for AI workloads involving datasets with billions of small image files or scientific applications generating millions of output files during simulation runs.

Intelligent Data Management and Tiering

Automated Hot Pools Tiering: Hot Pools technology analyzes access patterns using sophisticated heat algorithms that track both recency and frequency of file access. Data automatically migrates between NVMe flash and capacity disk based on usage, ensuring hot data resides on fast storage while cold data consumes cost-effective capacity tiers. Unlike traditional HSM requiring manual policies and stub file management, Hot Pools operates transparently with zero application modifications.

Policy-Based Data Orchestration: Stratagem provides flexible policy definitions based on file age, access patterns, user/group ownership, file size, and custom metadata tags. Organizations can implement sophisticated data lifecycle management including automatic archival to tape or object storage, compliance-driven retention policies, and project-based storage quotas.

GPU-Aware Hot Nodes Caching: Hot Nodes capability stages frequently accessed training datasets on local NVMe within GPU servers, dramatically reducing training iteration times by eliminating network latency for data access. EXAScaler’s filesystem intelligence manages cache population and coherency automatically, ensuring GPU nodes always access the most current data while minimizing shared storage traffic.

Client-Side Compression: DDN’s proprietary client-side compression reduces data footprint by 2-5x depending on content type, with AI training datasets typically achieving 2-3x reduction. Compression occurs at the client before network transmission, maintaining full storage system throughput while reducing capacity requirements and network bandwidth consumption.

Enterprise Reliability and Data Protection

End-to-End Data Integrity: Native T10 Data Integrity Field (DIF) implementation provides comprehensive data integrity verification from application layer through to physical storage media, detecting and preventing silent data corruption that could compromise research results or AI model accuracy.

High Availability Architecture: Redundant components throughout the system eliminate single points of failure, with automatic failover ensuring continuous operation even during hardware failures. Hot-swappable components enable maintenance without downtime, critical for environments requiring 24/7 availability.

Multi-Tenancy Support: Secure isolation between multiple organizations or projects sharing storage infrastructure enables service providers and large enterprises to consolidate workloads while maintaining strict security boundaries. Per-tenant quotas, performance guarantees, and access controls ensure fair resource sharing.

Comprehensive Security: Encryption in-flight and at-rest protects sensitive data, while detailed audit logging tracks all filesystem modifications for compliance requirements. Integration with enterprise authentication systems (LDAP, Active Directory, Kerberos) simplifies user management.

GPU Integration and AI Optimization

NVIDIA GPUDirect Storage Support: Deep integration with NVIDIA GPUDirect technology enables direct data transfers between storage and GPU memory, bypassing CPU and system memory to minimize latency and maximize GPU feeding efficiency. This capability proves particularly valuable for large-scale AI training where dataset loading represents a significant portion of iteration time.

Optimized for AI Frameworks: EXAScaler delivers optimized performance for popular AI frameworks including PyTorch, TensorFlow, JAX, and MXNet through tuned IO patterns and checkpoint management. Reference architectures developed in partnership with NVIDIA ensure optimal configuration for DGX systems and GPU clusters.

Checkpoint Performance: Large-scale AI training requires frequent checkpointing to enable fault recovery and experiment comparison. EXAScaler’s exceptional write bandwidth and metadata performance enable checkpoint operations completing in seconds rather than minutes, minimizing training interruption and improving overall GPU utilization.

Dataset Management: Efficient handling of AI datasets containing millions or billions of small files (images, preprocessed features) through optimized metadata performance and intelligent caching. Pre-staging capabilities enable loading entire datasets into high-performance tiers before training commences.

Operational Simplicity and Management

EXAScaler Management Framework (EMF): Centralized orchestration platform providing comprehensive cluster management, monitoring, and automation. EMF simplifies deployment of large-scale installations, automates routine operational tasks, and provides detailed health monitoring with predictive alerting.

Online Upgrades: Non-disruptive software updates enable organizations to remain current with the latest features and performance improvements without scheduling maintenance windows or impacting production workloads. This capability proves essential for installations supporting continuous operations.

Workload Analytics: Detailed visibility into filesystem utilization, performance characteristics, and job-level IO patterns enables capacity planning and performance optimization. Integration with job schedulers provides per-job IO attribution for chargeback and resource planning.

Flexible Deployment Options: Support for on-premises appliances, cloud deployments (EXAScaler Cloud for AWS, Azure, and Google Cloud), and hybrid architectures enables organizations to deploy consistent filesystem technology across diverse infrastructure environments.

Performance Benchmarks and Real-World Results

DDN EXAScaler consistently demonstrates industry-leading performance across standardized benchmarks and production deployments, validating its architectural advantages and optimization strategies.

IO500 Leadership

The IO500 benchmark represents the definitive measure of parallel filesystem performance, evaluating both bandwidth and metadata operations across diverse workload patterns. DDN maintains dominant performance in IO500 results:

10-Node Production List: DDN EXAScaler captures 7 of the top 10 positions in the production category, demonstrating not just peak performance but sustained capability in real-world configurations. Recent results show EXAScaler delivering over 3x the performance of WekaFS and more than 10x that of VAST Data on identical AI workloads.

Bandwidth Leadership: Sequential bandwidth tests show EXAScaler achieving 500+ GB/s aggregate throughput in large installations, enabling feeding of hundreds of GPUs simultaneously without storage bottlenecks. This performance proves critical for training large language models and computer vision workloads where data throughput directly impacts training time.

Metadata Excellence: Metadata-intensive workloads show EXAScaler delivering millions of operations per second, essential for AI applications working with billions of small files or scientific simulations generating massive numbers of output files.

MLPerf Storage Benchmarks

MLPerf Storage v2.0 benchmarks evaluate storage system performance under realistic AI training workloads using actual neural network architectures and data access patterns. DDN’s AI400X3 appliance achieved record-breaking results:

Unet3D Training (H100 GPUs): 120+ GB/s sustained read throughput supporting large-scale 3D medical imaging model training, demonstrating ability to keep multiple high-performance GPUs fully saturated during data-intensive training phases.

ResNet50 at Scale: Support for up to 640 simulated H100 GPUs training ResNet50 computer vision models, showing linear scalability of storage performance as GPU count increases—a critical requirement for organizations building large AI clusters.

BERT Language Model Training: 135+ simulated H100 GPUs supported for BERT natural language processing model training, with storage delivering sustained performance throughout multi-day training runs without degradation.

Production Deployment Results

Real-world deployments demonstrate EXAScaler’s capabilities beyond controlled benchmark scenarios:

NVIDIA Internal Clusters: NVIDIA exclusively uses DDN EXAScaler for internal GPU clusters and DGX SuperPOD installations, validating the platform’s ability to support cutting-edge AI research and development at massive scale. This endorsement from the GPU industry leader speaks volumes about EXAScaler’s capabilities.

Yotta Sovereign AI Cloud (India): DDN EXAScaler powers India’s sovereign AI cloud infrastructure, providing secure multi-tenant storage supporting government, public sector, and regulated industries. The deployment demonstrates EXAScaler’s ability to meet both performance and security requirements in mission-critical national infrastructure.

Scaleway European AI Cloud: Scaleway, a leading NVIDIA Cloud Partner in Europe, leverages EXAScaler to deliver high-performance AI cloud services to over 25,000 businesses, demonstrating the platform’s capabilities in commercial cloud environments requiring consistent performance and reliability.

Academic Supercomputing: Numerous Top500 supercomputers deploy EXAScaler for scientific computing workloads including climate modeling, molecular dynamics, computational fluid dynamics, and materials science simulations requiring petabyte-scale storage and sustained performance.

EXAScaler Product Family: Configurations and Options

DDN offers multiple EXAScaler configurations addressing diverse performance, capacity, and budget requirements across the spectrum of data-intensive applications.

AI400X3 – Ultimate Performance Platform

The AI400X3 represents DDN’s flagship all-NVMe storage appliance optimized for the most demanding AI and HPC workloads:

Key Advantages:

• Extreme performance density (70+ GB/s per rack unit)
• Lowest latency for time-sensitive workloads
• Minimal power consumption per GB/s (15x more efficient than previous generation)
• Ideal for GPU-intensive AI workloads requiring sustained high throughput

ES400X2 / AI400X2 – Balanced Hybrid Platform

The ES400X2 and AI400X2 series provide hybrid flash-disk architectures balancing performance with cost-effective capacity:

Key Advantages:

• Hot Pools automatic tiering optimizes flash utilization
• Cost-effective capacity for large datasets (genomics, satellite imagery, video archives)
• Flexible configurations supporting diverse workload mixes
• Lower TCO for workloads not requiring all-flash performance

ES200X2 – Compact High-Performance Option

The ES200X2 provides entry-level EXAScaler capabilities in space-constrained environments:

Key Advantages:

• Lower entry price point for organizations starting AI/HPC initiatives
• Compact footprint for space-constrained data centers
• Full EXAScaler feature set despite smaller scale
• Growth path to larger configurations as requirements evolve

EXAScaler Cloud – Public Cloud Deployments

EXAScaler Cloud extends the parallel filesystem capabilities to public cloud environments including AWS, Azure, and Google Cloud:

Google Cloud Managed Lustre: Fully-managed EXAScaler deployment on Google Cloud, integrated with Google Compute Engine and Google Kubernetes Engine. Provides cloud-native deployment in minutes with pay-as-you-go pricing.

Azure and AWS Options: Self-managed EXAScaler deployments on Azure and AWS using cloud-native storage (EBS, Azure Disk) with marketplace images simplifying setup. Enables consistent filesystem technology across on-premises and cloud environments.

Hybrid Cloud Architectures: Unified namespace spanning on-premises EXAScaler and cloud deployments enables seamless workload migration and cloud bursting scenarios. Organizations can develop models on-premises and train at scale in the cloud using identical filesystem infrastructure.

Use Cases and Application Scenarios

DDN EXAScaler addresses diverse data-intensive workload requirements across industries and application domains, delivering proven value in production environments worldwide.

Large-Scale AI Model Training

Foundation Model Development: Training large language models, computer vision transformers, and multi-modal AI systems requires sustained high-bandwidth access to massive datasets (petabytes) containing billions of training examples. EXAScaler’s parallel architecture feeds hundreds of GPUs simultaneously, eliminating storage as a bottleneck and maximizing expensive GPU utilization.

Distributed Training at Scale: Modern AI training distributes across dozens or hundreds of GPUs using data parallelism and model parallelism strategies. EXAScaler’s low-latency, high-throughput characteristics ensure efficient gradient synchronization and checkpoint management critical for distributed training success.

Experiment Management: AI research involves running hundreds or thousands of training experiments with varying hyperparameters, architectures, and datasets. EXAScaler’s exceptional metadata performance and capacity scalability enable organizations to maintain comprehensive experiment histories supporting reproducibility and regulatory compliance.

High-Performance Computing and Scientific Research

Molecular Dynamics Simulations: Computational chemistry and drug discovery applications generate massive trajectory files requiring sustained write bandwidth and efficient access for post-processing analysis. EXAScaler supports workflows processing petabytes of simulation data across thousands of compute nodes.

Climate and Weather Modeling: Earth science applications ingest satellite observations, run complex atmospheric simulations, and produce petabytes of output requiring long-term retention and analysis. EXAScaler’s hybrid tiering ensures frequently accessed data resides on flash while historical archives consume cost-effective capacity tiers.

Computational Fluid Dynamics: Engineering simulation and analysis workflows generate massive datasets requiring both high write bandwidth during simulation and high read bandwidth during visualization and post-processing. EXAScaler’s bidirectional performance supports these diverse IO patterns efficiently.

Genomics and Bioinformatics: Next-generation sequencing generates terabytes of raw data per instrument run, requiring sustained ingest bandwidth and subsequent parallel processing for variant calling, assembly, and analysis. EXAScaler’s scalability enables organizations to consolidate multiple genomics pipelines on shared infrastructure.

Media and Entertainment Production

Visual Effects and Animation: Film and television production involves massive high-resolution imagery and video requiring simultaneous access by dozens of artists and rendering systems. EXAScaler provides the bandwidth and concurrency supporting collaborative creative workflows.

Real-Time Rendering: Modern game development and virtual production leverage real-time rendering requiring low-latency access to multi-terabyte asset libraries. EXAScaler’s Hot Nodes caching enables local staging of frequently accessed assets on workstations and render nodes.

Media Archive and MAM: Long-term preservation of media assets requires petabyte-scale capacity with efficient search and retrieval capabilities. EXAScaler’s metadata performance enables media asset management systems to maintain comprehensive catalogs across massive archives.

Financial Services and Analytics

Risk Modeling and Simulation: Monte Carlo simulations and risk analysis workloads generate massive datasets requiring sustained write bandwidth and subsequent parallel processing. EXAScaler supports workflows running thousands of simulation jobs concurrently.

Algorithmic Trading Development: Backtesting trading strategies against years of historical market data requires rapid access to terabytes of time-series information. EXAScaler’s low latency and high bandwidth enable faster iteration cycles during strategy development.

Fraud Detection and AML: Real-time fraud detection systems process massive transaction volumes requiring low-latency access to feature stores and model artifacts. EXAScaler’s performance characteristics support latency-sensitive production AI applications.

Autonomous Vehicle Development

Sensor Data Ingestion: Autonomous vehicle test fleets generate terabytes of sensor data (camera, lidar, radar) daily requiring sustained ingest bandwidth and long-term retention. EXAScaler’s write performance supports parallel data streams from multiple vehicle sources.

Model Training and Validation: Training perception models on petabyte-scale sensor datasets requires feeding dozens or hundreds of GPUs with diverse training examples. EXAScaler’s parallel architecture eliminates storage bottlenecks during distributed training.

Simulation and Testing: Virtual testing environments generate synthetic sensor data and require rapid access to scenario databases. EXAScaler supports high-throughput simulation workloads accelerating development cycles.

Deployment Considerations and Best Practices

Successful EXAScaler deployments require careful planning addressing network infrastructure, compute integration, capacity sizing, and operational procedures.

Network Architecture Requirements

High-Bandwidth Interconnects: EXAScaler deployments require dedicated high-speed networks separate from general data center traffic. Recommended options include:

• HDR InfiniBand (200 Gb/s): Preferred for largest deployments requiring lowest latency and highest efficiency
• 100/200GbE Ethernet: Suitable for most installations with properly configured RoCE (RDMA over Converged Ethernet)
• Non-Blocking Fabric: Ensure sufficient oversubscription ratios (1:1 or 2:1) between storage and compute

Client Connectivity: Each compute node should have dedicated storage network connectivity matching its IO requirements. GPU-heavy AI nodes benefit from multiple network interfaces (2-4x 100GbE or 2x HDR InfiniBand) supporting aggregate throughput matching GPU data consumption rates.

Management Network Separation: Maintain separate management networks for storage administration, monitoring, and out-of-band access independent of high-speed data networks. This separation ensures administrative access during storage network maintenance or issues.

Compute Integration Strategies

GPU Cluster Design: Align storage and compute architecture ensuring balanced ratios. General guidelines:

• 1 GB/s storage bandwidth per 8-10 GPUs (training workloads)
• 2-3 GB/s storage bandwidth per 8-10 GPUs (data-intensive workloads)
• Adjust based on specific model architectures and dataset characteristics

Hot Nodes Implementation: Reserve 2-4TB of local NVMe per GPU server for Hot Nodes caching when deploying this feature. Ensure compute nodes run EXAScaler client software supporting cache coherency protocols.

Checkpoint Storage Planning: Size checkpoint capacity based on model size multiplied by checkpoint frequency. Large language model training may require 10-20TB per training run for checkpoint retention supporting experiment comparison and recovery.

Capacity Planning and Sizing

Workload Characterization: Understand dataset sizes, access patterns, retention requirements, and growth projections:

• Training dataset sizes (working set requiring high-performance access)
• Historical data retention (archive tier candidates)
• Scratch space for intermediate results (temporary capacity)
• Checkpoint and experiment artifact storage

Performance Requirements: Quantify bandwidth and IOPS requirements:

• Peak aggregate bandwidth (sum of all concurrent client requirements)
• Sustained bandwidth (typical operational loads)
• Metadata operations per second (file creates, stats, deletes)
• Latency sensitivity (real-time inference vs batch training)

Growth Planning: Size initial deployment to meet immediate needs while planning expansion:

• Deploy 50-70% of anticipated 2-year capacity initially
• Plan expansion increments (appliances added as capacity or performance requirements grow)
• Consider hybrid configurations starting with all-flash and adding capacity tiers as datasets age

Data Management Policies

Hot Pools Configuration: Define tiering policies matching workload characteristics:

• Aggressive Flash Retention: Keep frequently accessed data on flash (30-90 days of access history)
• Capacity Optimization: Migrate to capacity tiers after short flash residence (7-30 days)
• Custom Policies: Define project-specific policies based on funding, priorities, or regulatory requirements

Quota Management: Implement quotas preventing individual users or projects from consuming disproportionate resources:

• User quotas limiting per-user consumption
• Group/project quotas enabling team-based allocation
• Directory quotas for specific workload isolation

Backup and Archive: Define data protection strategies:

• Stratagem policies automating archival to tape or object storage
• Snapshot schedules for point-in-time recovery
• Replication to secondary sites for disaster recovery

Total Cost of Ownership and Economic Benefits

Understanding EXAScaler’s TCO requires examining direct costs, operational efficiencies, and productivity improvements extending beyond hardware acquisition pricing.

Direct Cost Components

Hardware Acquisition: EXAScaler pricing varies based on configuration, capacity, and performance tier:

• All-NVMe AI400X3: Premium pricing reflecting extreme performance density
• Hybrid ES400X2: Balanced pricing for mixed performance/capacity requirements
• Entry ES200X2: Lower initial investment for smaller deployments

Pricing typically includes storage appliances, network adapters, deployment services, and software licenses. Organizations should request detailed quotes from authorized DDN partners or contact DDN directly for accurate pricing.

Software and Support: Annual maintenance contracts provide software updates, technical support, and hardware warranty coverage. Typical costs range 15-20% of hardware value annually, delivering substantial value through access to continuous feature enhancements and expert support.

Network Infrastructure: Budget for high-speed networking equipment including InfiniBand or Ethernet switches, cables, and transceivers. Network costs represent 10-20% of total storage investment depending on scale and technology choices.

Operational Cost Advantages

Power Efficiency: DDN’s AI400X3 delivers 15x better performance per watt compared to previous-generation storage, dramatically reducing electricity costs in large installations. A system delivering 100 GB/s aggregate bandwidth might consume 20-30 kW versus 300-400 kW for equivalent performance from older technologies.

Cooling Requirements: Reduced power consumption translates directly to lower cooling costs, with modern data center cooling requiring 0.5-1.0 watt of cooling per watt of IT equipment. Organizations deploying AI400X3 realize 10-15x reductions in cooling costs per unit of performance.

Density Advantages: Higher performance density reduces data center space requirements, with AI400X3 delivering 70+ GB/s per rack unit compared to 5-10 GB/s for traditional storage. Space savings matter significantly in capacity-constrained data centers where square footage costs $1000-3000 per square foot annually.

Operational Simplicity: EXAScaler’s management framework and automation features reduce staffing requirements compared to building and maintaining custom Lustre installations. Organizations typically report 40-60% reductions in storage administration overhead after migrating to EXAScaler from DIY parallel filesystems.

Productivity and Business Value

GPU Utilization Improvements: Eliminating storage bottlenecks improves GPU utilization from typical 40-60% levels to 80-90%+, effectively increasing compute capacity without purchasing additional GPUs. For organizations with thousands of GPUs representing tens or hundreds of millions of dollars in investment, utilization improvements deliver massive ROI.

Reduced Training Time: Faster data access accelerates AI model training by 20-50% depending on workload characteristics, enabling more experiments per day and faster time-to-production for AI applications. Organizations report 2-3x increases in researcher productivity after deploying EXAScaler.

Simplified Operations: Turnkey appliances with comprehensive management tools eliminate the complexity of building and operating custom storage infrastructure, freeing IT staff to focus on higher-value activities. Organizations migrating from DIY storage to EXAScaler frequently redeploy storage administrators to application support roles.

Business Agility: Rapid deployment (days rather than months for custom builds) and flexible scaling enable organizations to respond quickly to business opportunities. Research institutions report securing competitive grants due to ability to rapidly deploy infrastructure supporting proposal requirements.

TCO Comparison Example

Consider a 500-GPU AI cluster requiring 100 GB/s storage bandwidth:

EXAScaler AI400X3 Configuration:

• 8x AI400X3 appliances: ~$4.0M
• Network infrastructure: ~$600K
• 5-year support: ~$3.5M
• Power (5 years @ $0.12/kWh): ~$900K
• Space (5 years @ $2000/sq ft): ~$300K
• Total 5-Year TCO: ~$9.3M

Alternative Traditional Storage:

• 40x traditional storage nodes: ~$3.5M
• Network infrastructure: ~$1.2M
• DIY Lustre deployment services: ~$400K
• 5-year support: ~$2.8M
• Power (5 years @ $0.12/kWh): ~$5.4M (6x higher consumption)
• Space (5 years @ $2000/sq ft): ~$1.8M (6x larger footprint)
• Additional IT staff (5 years): ~$2.5M (2 additional FTEs)
• Total 5-Year TCO: ~$17.6M

EXAScaler Advantage: 47% lower TCO over 5 years despite higher initial hardware costs, driven primarily by operational efficiencies, power savings, and eliminated staffing requirements.

Integration with Leading AI and HPC Platforms

DDN EXAScaler integrates seamlessly with industry-leading compute platforms, providing validated reference architectures simplifying deployment and ensuring optimal performance.

NVIDIA DGX and GPU Systems

NVIDIA DGX SuperPOD: DDN serves as the exclusive storage partner for NVIDIA DGX SuperPOD installations, delivering validated configurations that meet NVIDIA’s stringent performance and reliability requirements. Reference architectures provide detailed deployment guides, performance tuning recommendations, and configuration management automation.

NVIDIA DGX BasePOD: Smaller-scale DGX deployments leverage EXAScaler configurations scaled appropriately for 8-32 DGX systems, delivering the same architecture and management capabilities as SuperPOD installations at smaller scale.

NVIDIA HGX-Based Servers: Organizations building custom GPU servers using NVIDIA HGX platforms benefit from DDN reference architectures addressing various server OEM implementations including Supermicro, HPE, Dell, and Lenovo systems.

GPUDirect Storage Integration: EXAScaler fully supports NVIDIA GPUDirect Storage technology enabling direct data transfers between storage and GPU memory, minimizing CPU overhead and maximizing throughput efficiency. This integration proves particularly valuable for large-scale training workloads.

Major Server OEM Platforms

HPE Cray Supercomputers: DDN provides storage for numerous Cray EX supercomputer installations, delivering the performance and scalability required by exascale-class systems running mission-critical scientific applications.

Dell PowerEdge Servers: Reference architectures address Dell PowerEdge GPU servers and HPC clusters, providing validated configurations and tuning guidance ensuring optimal interoperability.

Supermicro GPU Systems: Joint solutions with Supermicro span from compact AI edge appliances through large-scale data center installations, addressing the full spectrum of AI infrastructure requirements.

Lenovo ThinkSystem: Validated configurations supporting Lenovo ThinkSystem servers including their AI-optimized SR680a and SR650 platforms, delivering turnkey solutions for enterprises standardizing on Lenovo infrastructure.

Cloud Service Provider Integration

Google Cloud Platform: Google Cloud Managed Lustre, powered by DDN EXAScaler, provides fully-managed parallel filesystem services integrated with Google Compute Engine, Google Kubernetes Engine, and Cloud Storage. Organizations can deploy high-performance filesystems in minutes with pay-as-you-go pricing.

Microsoft Azure: EXAScaler Cloud for Azure enables organizations to deploy parallel filesystems on Azure infrastructure, integrating with Azure Machine Learning, Azure Batch, and Azure HPC Cache for comprehensive hybrid cloud architectures.

Amazon Web Services: Self-managed EXAScaler deployments on AWS leverage Amazon EC2 instances and Amazon EBS volumes, providing parallel filesystem capabilities for AWS-based AI and HPC workloads.

AI Framework and Software Ecosystem

PyTorch and TensorFlow: Optimized configurations and tuning guidance ensure maximum performance when training models using popular AI frameworks. DDN works closely with framework developers identifying and addressing IO bottlenecks.

NVIDIA AI Enterprise: Full compatibility with NVIDIA AI Enterprise software suite including NVIDIA Base Command orchestration, NVIDIA AI Workbench development environments, and NVIDIA NIM microservices.

Kubernetes and Container Orchestration: Container Storage Interface (CSI) drivers enable EXAScaler integration with Kubernetes, OpenShift, and other container orchestration platforms, supporting cloud-native AI development workflows.

Job Schedulers: Integration with Slurm, PBS Pro, LSF, and other HPC job schedulers provides workload management capabilities and enables job-level IO performance monitoring and attribution.

Competitive Advantages and Market Position

DDN EXAScaler holds the leadership position in parallel filesystem storage for AI and HPC applications, consistently outperforming competitive solutions across performance, scalability, and operational dimensions.

Performance Leadership

IO500 Dominance: DDN captures 7 of the top 10 positions in IO500 10-node production benchmarks, demonstrating not just peak performance capabilities but sustained real-world performance across diverse workload patterns. Recent results show EXAScaler delivering 3x better performance than WekaFS and 10x better than VAST Data on identical AI workloads.

MLPerf Storage Records: DDN AI400X3 achieved record-breaking results in MLPerf Storage v2.0 benchmarks, setting the standard for AI storage performance across training and inference workloads. These results validate EXAScaler’s ability to keep hundreds of GPUs fully utilized during training.

Proven Scalability: Installations spanning hundreds of EXAScaler appliances delivering exabyte-scale capacity and multi-TB/s aggregate throughput demonstrate proven scalability beyond what alternative solutions can achieve. The world’s largest AI and HPC installations trust EXAScaler for their most demanding workloads.

Operational Advantages

Turnkey Simplicity: Unlike competitors requiring extensive custom configuration or offering only software solutions, EXAScaler delivers fully-integrated appliances with comprehensive management tools, dramatically reducing deployment time and operational complexity.

Enterprise Support: DDN’s global support organization provides 24/7 expert assistance, with response times measured in minutes for critical issues. Competitors offering software-only solutions leave customers responsible for integration, troubleshooting, and operations.

Continuous Innovation: Regular software updates deliver new features and performance improvements without requiring forklift upgrades. Online upgrade capability enables organizations to remain current without scheduling maintenance windows or impacting production workloads.

Strategic Partnerships

NVIDIA Exclusive Partnership: NVIDIA’s exclusive use of DDN EXAScaler for internal GPU clusters and DGX SuperPOD installations represents the strongest possible validation of EXAScaler’s capabilities. This partnership ensures EXAScaler remains optimized for latest NVIDIA GPU technologies.

OEM Ecosystem: Validated configurations with every major server OEM simplify procurement and ensure interoperability. Organizations can purchase complete solutions from preferred vendors with confidence in DDN validation and support.

Cloud Provider Integrations: Managed services on Google Cloud and self-service options on Azure and AWS enable organizations to deploy EXAScaler capabilities in cloud environments, supporting hybrid cloud architectures and cloud bursting scenarios.

Customer Success Stories

Organizations across industries and geographies have achieved transformational results deploying DDN EXAScaler, realizing performance improvements, cost savings, and operational efficiencies enabling AI and HPC initiatives previously impossible with traditional storage infrastructure.

NVIDIA – Internal Infrastructure

NVIDIA, the global leader in AI computing, exclusively relies on DDN EXAScaler for internal GPU clusters supporting AI research, autonomous vehicle development, graphics technology advancement, and data center product validation. This deployment spans thousands of GPUs and multiple petabytes of storage, representing one of the world’s most demanding AI infrastructure environments.

Results:

• Sustained GPU utilization exceeding 85% during training campaigns
• Elimination of storage as bottleneck enabling rapid experimental iteration
• Foundation for NVIDIA’s AI breakthroughs in conversational AI, computer vision, and generative AI

NVIDIA Executive Endorsement: “I have a very simple statement for you; NVIDIA uses DDN.” – Manuvir Das, Head of Enterprise Computing, NVIDIA

Yotta – India Sovereign AI Initiative

Yotta Infrastructure deployed DDN EXAScaler as the storage foundation for India’s sovereign AI cloud, supporting government, public sector, and regulated industries requiring secure, high-performance AI capabilities within national boundaries. The deployment enables multi-tenant AI development while maintaining strict data sovereignty and security requirements.

Results:

• Secure multi-tenant environment supporting dozens of organizations simultaneously
• Performance enabling training of large language models and computer vision systems
• Foundation for India’s AI independence and technology leadership

Scaleway – European AI Cloud Provider

Scaleway, a leading NVIDIA Cloud Provider in Europe serving over 25,000 businesses, deployed DDN EXAScaler to power their AI cloud infrastructure including the Nabu 2023 DGX H100 cluster. EXAScaler enables Scaleway to deliver high-performance AI cloud services with enterprise-grade reliability and security.

Results:

• Rapid AI service delivery supporting customer time-to-market requirements
• Consistent performance meeting SLA commitments across diverse workloads
• Competitive differentiation through superior storage performance

Academic and Research Institutions

Numerous research universities and national laboratories deploy EXAScaler supporting scientific discovery across domains including climate science, materials research, genomics, particle physics, and computational chemistry.

Representative Installations:

• Multiple Top500 supercomputers rely on EXAScaler for primary research storage
• Genomics centers process thousands of samples daily using EXAScaler-backed pipelines
• Climate research facilities manage petabyte-scale observation and simulation datasets
• Particle physics collaborations (CERN, Fermilab) leverage EXAScaler for detector data analysis

Frequently Asked Questions

What is DDN EXAScaler and how does it differ from standard Lustre?

DDN EXAScaler is a turnkey parallel file system appliance combining DDN’s high-performance storage hardware with an enhanced version of Lustre filesystem software. Unlike community Lustre or basic Lustre implementations, EXAScaler includes proprietary DDN enhancements unavailable elsewhere: Stratagem data orchestration for policy-based data management, Hot Pools automated tiering between flash and disk, Hot Nodes GPU-aware caching, client-side compression, and the EXAScaler Management Framework for simplified operations. EXAScaler delivers enterprise-grade capabilities with operational simplicity impossible to achieve with DIY Lustre deployments.

What performance can I expect from EXAScaler?

Performance varies based on configuration, but typical installations deliver:

• Individual AI400X3 appliances: 140 GB/s read, 110 GB/s write, 4M IOPS in 2U
• Mid-scale deployments (8-16 appliances): 500-1500 GB/s aggregate bandwidth
• Large-scale installations (50+ appliances): Multi-TB/s aggregate throughput
• Latency: Sub-millisecond for cached data, single-digit milliseconds for remote access
• Scalability: Linear performance scaling as appliances are added

Real-world results consistently show EXAScaler delivering 3-10x better performance than alternative parallel filesystems across diverse AI and HPC workloads, as validated by IO500 and MLPerf benchmarks.

How does EXAScaler integrate with NVIDIA GPUs and DGX systems?

EXAScaler provides deep integration with NVIDIA GPU platforms through multiple mechanisms:

• GPUDirect Storage Support: Enables direct data transfers between storage and GPU memory, bypassing CPU and system memory for maximum efficiency
• Hot Nodes Caching: Automatically stages frequently accessed training data on local NVMe within GPU servers, minimizing access latency
• Reference Architectures: DDN and NVIDIA jointly develop validated configurations for DGX SuperPOD, DGX BasePOD, and HGX-based systems
• Performance Optimization: Tuned configurations ensure optimal performance for AI frameworks (PyTorch, TensorFlow) running on NVIDIA GPUs
• NVIDIA Validation: NVIDIA exclusively uses DDN EXAScaler for internal GPU clusters, providing the strongest possible validation

Organizations deploying NVIDIA GPU systems benefit from tested configurations and best practices ensuring immediate productivity without extended tuning periods.

What capacity and performance scaling options does EXAScaler support?

EXAScaler provides flexible scaling across multiple dimensions:

Capacity Scaling:

• Start small (10s of TBs) and grow incrementally to exabyte scale
• Add appliances non-disruptively as capacity requirements evolve
• Hybrid configurations combining flash and disk enable cost-effective scaling
• No architectural limits on filesystem size or file count

Performance Scaling:

• Near-linear bandwidth scaling as appliances are added
• Concurrent user scaling supporting thousands of simultaneous clients
• IOPS scaling to millions of operations per second at large scale
• Metadata performance scaling independently through dedicated MDT resources

Deployment Flexibility:

• Single appliance departmental installations
• Multi-rack data center deployments
• Distributed installations across multiple data centers
• Hybrid on-premises/cloud configurations

This architectural flexibility enables organizations to right-size initial deployments and scale predictably as requirements grow, avoiding over-provisioning or capacity constraints.

How does Hot Pools tiering work and what benefits does it provide?

Hot Pools automates data movement between performance and capacity storage tiers based on access patterns:

Operation:

• Monitors file access frequency and recency using sophisticated heat algorithms
• Automatically migrates frequently accessed (“hot”) data to NVMe flash
• Moves infrequently accessed (“cold”) data to cost-effective capacity disk
• Operates transparently without application modifications or user intervention
• Maintains full POSIX semantics throughout tiering process

Benefits:

• Optimizes flash utilization by ensuring only active data resides on expensive storage
• Reduces TCO by enabling smaller flash configurations without performance compromise
• Eliminates manual data movement and storage management overhead
• Adapts automatically to changing workload patterns
• Provides better economics than pure flash or pure disk approaches

Organizations typically achieve 60-80% reduction in flash capacity requirements while maintaining performance equivalent to all-flash systems, delivering substantial cost savings without operational complexity.

What networking infrastructure does EXAScaler require?

EXAScaler requires dedicated high-speed networks separate from general data center traffic:

Recommended Options:

• HDR/NDR InfiniBand (200-400 Gb/s): Optimal for largest deployments requiring minimum latency
• 100/200GbE Ethernet with RoCE: Suitable for most installations with proper configuration
• Multiple Links per Appliance: 4-8 network connections per appliance depending on model

Network Design Considerations:

• Non-blocking or minimally oversubscribed fabric (1:1 or 2:1 oversubscription)
• Separate management network for administration and monitoring
• Client connectivity matching IO requirements (2-4x 100GbE or 2x HDR InfiniBand for GPU nodes)
• Jumbo frames enabled (9000 byte MTU) for efficiency

Organizations should budget 10-20% of storage investment for networking infrastructure including switches, adapters, cables, and transceivers. DDN provides reference network architectures simplifying design and procurement.

What is the typical deployment timeline for EXAScaler?

Deployment timelines vary based on scale and environment complexity:

Small Deployments (1-4 appliances):

• Planning and ordering: 2-4 weeks
• Hardware delivery: 4-8 weeks
• Installation and configuration: 3-5 days
• Validation and tuning: 1-2 weeks
• Total: 8-14 weeks from order to production

Medium Deployments (5-16 appliances):

• Planning and ordering: 4-6 weeks
• Hardware delivery: 6-10 weeks
• Installation and configuration: 1-2 weeks
• Validation and tuning: 2-3 weeks
• Total: 13-21 weeks from order to production

Large Deployments (16+ appliances):

• Planning and ordering: 6-8 weeks
• Hardware delivery: 8-12 weeks (phased delivery possible)
• Installation and configuration: 2-4 weeks
• Validation and tuning: 3-4 weeks
• Total: 19-28 weeks from order to production

DDN’s professional services team can accelerate deployment through proven deployment methodologies, automated configuration tools, and experienced engineering support. Organizations with urgent requirements should discuss expedited options with DDN sales.

How does EXAScaler pricing compare to alternatives?

EXAScaler typically commands premium pricing compared to traditional storage due to extreme performance capabilities, but delivers superior TCO when evaluating complete lifecycle costs:

Initial Acquisition:

• Higher hardware cost per TB compared to traditional storage
• Lower hardware cost per GB/s compared to achieving equivalent performance with alternatives
• Integrated appliances eliminate custom integration costs

Operational Costs:

• 10-15x better power efficiency reduces electricity costs dramatically
• Reduced data center space requirements (higher performance density)
• 40-60% lower administration costs compared to DIY Lustre
• Fewer required appliances to meet performance targets

Productivity Value:

• Eliminates storage bottlenecks improving GPU utilization by 20-40%
• Accelerates training reducing time-to-insight and improving researcher productivity
• Enables capabilities impossible with alternative storage (supporting largest AI models)

Organizations should evaluate TCO over 3-5 year periods rather than focusing on acquisition costs, as operational efficiencies and productivity improvements frequently deliver 30-50% TCO advantages despite higher initial pricing.

What support and services does DDN provide?

DDN offers comprehensive support and professional services ensuring customer success:

Technical Support:

• 24/7/365 global support with rapid response times (minutes for critical issues)
• Multi-tier escalation process with access to development engineers
• Proactive monitoring and health checks identifying issues before failures
• Regular software updates and security patches

Professional Services:

• Deployment planning and design services
• On-site installation and configuration support
• Performance tuning and optimization consulting
• Training for administrators and users
• Migration services from alternative storage platforms
• Capacity planning and architecture reviews

Support Tiers:

• Standard support included with maintenance contracts
• Premium support options for mission-critical environments
• Dedicated support representatives for strategic accounts

Community Resources:

• Customer portal with documentation and knowledge base
• User forums and community support
• Regular user group meetings and technical workshops
• Online training and certification programs

Organizations can depend on DDN’s deep expertise in parallel filesystems and decades of experience supporting the world’s most demanding HPC and AI installations.

Conclusion: The Storage Foundation for AI and HPC Leadership

DDN EXAScaler represents the convergence of three decades of parallel filesystem innovation, purpose-built hardware optimized for data-intensive workloads, and enterprise-grade management capabilities that eliminate traditional operational complexity. As organizations worldwide race to leverage artificial intelligence, machine learning, and high-performance computing for competitive advantage, storage infrastructure has emerged from relative obscurity to occupy the critical path determining success or failure of these strategic initiatives.

The evidence validates EXAScaler’s leadership position unequivocally. NVIDIA’s exclusive adoption for internal GPU clusters, dominant performance across industry benchmarks (IO500, MLPerf Storage), deployment in the world’s most prestigious supercomputing facilities, and trust placed by governments building sovereign AI infrastructure all speak to capabilities transcending marketing claims to deliver measurable, transformational results in production environments.

For organizations evaluating storage infrastructure for AI factories, HPC clusters, or data-intensive research facilities, DDN EXAScaler merits serious consideration as the platform capable of delivering both immediate performance advantages and long-term strategic value. The combination of extreme performance, intelligent automation, operational simplicity, and proven scalability creates a solution addressing not just today’s requirements but anticipating tomorrow’s challenges as AI models grow ever larger and computational demands continue their exponential trajectory.

To explore DDN EXAScaler for your AI and HPC infrastructure requirements, contact DDN directly or engage with authorized DDN partners for detailed technical discussions, customized configurations, and accurate pricing tailored to your specific workload characteristics and business objectives.

Additional Resources:

• DDN EXAScaler Product Page
• AI Storage Solutions Overview
• GPU ROI Calculator
• DDN Reference Architectures

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Last update at December 2025