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AI Infrastructure

NetApp AIPod

Enterprise-grade AI infrastructure combining NVIDIA DGX supercomputers with NetApp storage

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Category
Software
Ideal For
Enterprises
Deployment
On-premise / Hybrid
Integrations
None+ Apps
Security
Enterprise-grade data protection, access controls, audit logging
API Access
Yes - programmatic infrastructure management and monitoring

About NetApp AIPod

NetApp AIPod, powered by ONTAP AI, is a comprehensive integrated solution designed to accelerate artificial intelligence and deep learning initiatives at enterprise scale. The platform combines NVIDIA DGX-1 supercomputers with NetApp AFF high-performance storage and Cisco networking into a verified, production-ready architecture. ONTAP AI delivers exceptional performance for training and inference workloads while maintaining data accessibility and security. The solution enables organizations to rapidly deploy AI infrastructure without architectural complexity. AiDOOS enhances the AIPod offering by providing marketplace governance, resource optimization, and seamless integration with enterprise deployment frameworks. Organizations leverage AiDOOS to standardize AI infrastructure provisioning, ensure optimal resource utilization across multiple AIPod instances, and streamline governance policies. The platform supports hybrid cloud deployments, enabling flexible scaling and workload distribution across on-premise and cloud environments while maintaining consistent performance and management.

Challenges It Solves

  • Complex AI infrastructure deployment requiring specialized expertise and integration
  • Data bottlenecks limiting GPU utilization and model training performance
  • Difficulty scaling AI initiatives without significant capital expenditure
  • Lack of unified management across distributed AI computing resources
  • Storage performance limitations preventing efficient deep learning workflows

Proven Results

89
Faster model training cycles with optimized data throughput
72
Reduced infrastructure deployment time and complexity
67
Improved GPU utilization and compute efficiency

Key Features

Core capabilities at a glance

Integrated Compute-Storage Architecture

Optimized data flow eliminates performance bottlenecks

Up to 10x faster data access for training workloads

NVIDIA DGX-1 Integration

Enterprise-grade GPU computing with verified compatibility

8 V100 GPUs per node for parallel deep learning

NetApp AFF Storage Performance

Ultra-fast NVMe storage with enterprise reliability

Sub-millisecond latency and 99.999% availability

Unified Management Console

Simplified operations across compute and storage resources

Reduced operational overhead by 60%

Scalable Architecture

Grow infrastructure capacity without redesign

Linear performance scaling across multiple nodes

Enterprise Data Protection

Integrated backup and disaster recovery capabilities

Protect critical AI models and training datasets

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Real-World Use Cases

See how organizations drive results

Large-Scale Model Training
Accelerate training of large neural networks and transformers using distributed computing across DGX nodes with direct storage access.
85
Training time reduced by 75-85% versus standard infrastructure
Real-Time Inference Deployment
Deploy production inference workloads requiring low-latency data access and high throughput for serving predictions at scale.
92
Sub-100ms latency inference at high concurrency
Research and Development
Provide research teams with high-performance infrastructure for experimentation and algorithm development across AI domains.
78
Iteration cycles accelerated by 70+ percent
Enterprise Data Analytics
Process massive datasets for analytics, machine learning feature engineering, and predictive model development.
81
Analytics query performance improved 3-4x
Multi-Tenant AI Services
Support multiple teams and projects with resource isolation, quota management, and dedicated performance guarantees.
76
Resource utilization increased to 88% efficiency

Integrations

Seamlessly connect with your tech ecosystem

N

NVIDIA GPU Cloud

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Access containerized AI frameworks and pretrained models optimized for DGX-1 hardware

C

Cisco UCS Computing

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Seamless network integration with verified Cisco switching and fabric architecture

K

Kubernetes

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Container orchestration for workload scheduling and resource management across AIPod clusters

A

Apache Spark

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Distributed data processing for ETL pipelines feeding AI training workloads

T

TensorFlow / PyTorch

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Full compatibility with major deep learning frameworks for model development and training

N

NetApp Cloud Sync

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Hybrid cloud data synchronization for multi-location AI infrastructure

A

AiDOOS Marketplace

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Governance, resource allocation, and deployment automation across AIPod infrastructure

S

Splunk / ELK Stack

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Comprehensive monitoring and logging of compute and storage performance metrics

Implementation with AiDOOS

Outcome-based delivery with expert support

Outcome-Based

Pay for results, not hours

Milestone-Driven

Clear deliverables at each phase

Expert Network

Access to certified specialists

Implementation Timeline

1
Discover
Requirements & assessment
2
Integrate
Setup & data migration
3
Validate
Testing & security audit
4
Rollout
Deployment & training
5
Optimize
Performance tuning

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Alternatives & Comparisons

Find the right fit for your needs

Capability NetApp AIPod atbridges Contus Bot Zomani Content Writ…
Customization Excellent Good Excellent Excellent
Ease of Use Good Good Good Excellent
Enterprise Features Excellent Excellent Good Good
Pricing Fair Fair Fair Good
Integration Ecosystem Excellent Excellent Good Excellent
Mobile Experience Fair Good Good Good
AI & Analytics Excellent Excellent Excellent Excellent
Quick Setup Good Good Good Excellent

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Frequently Asked Questions

What is the typical deployment timeline for NetApp AIPod?
Most deployments complete in 2-4 weeks including hardware setup, ONTAP configuration, and workload optimization. AiDOOS accelerates post-deployment governance and workload orchestration setup.
Can AIPod scale beyond a single node?
Yes. AIPod architectures support multi-node clusters for distributed training. AiDOOS marketplace provides centralized management and resource allocation across multiple AIPod instances.
What frameworks and tools are compatible with AIPod?
ONTAP AI supports TensorFlow, PyTorch, Keras, CUDA, and all major deep learning frameworks. Integration with Kubernetes enables flexible workload orchestration across the infrastructure.
How does AiDOOS enhance the AIPod experience?
AiDOOS provides marketplace governance, automated resource provisioning, cost allocation, and multi-team quota management across AIPod infrastructure, simplifying enterprise deployment and optimization.
What data protection features are included?
NetApp AFF provides RAID, snapshots, replication, and disaster recovery capabilities. ONTAP supports encryption and compliance features for regulated industries.
Is hybrid cloud deployment supported?
Yes. NetApp Cloud Sync enables seamless data synchronization between on-premise AIPod and cloud resources, supporting flexible hybrid AI infrastructure strategies.