DDN Infinia: The Foundation for Enterprise AI Success

Executive Summary

In an era where AI defines competitive advantage, DDN Infinia emerges as a transformative data platform—one that doesn’t just overcome obstacles but reimagines what’s achievable. Designed in collaboration with NVIDIA and xAI, Infinia goes beyond incremental progress, delivering substantial gains in efficiency and workload performance, reducing complexity to unlock meaningful business value from AI. It’s a solution for organizations ready to harness data at scales that rival today’s largest hyperscalers.

Business leaders face mounting pressure to maximize AI investments while avoiding costly delays and inefficiencies. Infinia addresses these challenges head-on. Its metadata-driven architecture streamlines even the most complex AI data demands—unifying data distributed between core-cloud-edge, eliminating infrastructure bottlenecks, optimizing GPU utilization, reducing operational costs, and paving the way for impactful AI innovation. This isn’t about keeping pace; it’s about setting a new standard for what AI can deliver.

But Infinia doesn’t stop at infrastructure—it moves above the stack. Designed as a cloud-native, software-defined data intelligence platform, Infinia seamlessly bridges AI execution engines, data orchestration tools, and developer workflows. It supports native access to object, file, and block storage while integrating directly with AI frameworks like Spark, TensorFlow, and Ray. Its powerful event engine and distributed SQL query service enable real-time automation, intelligent indexing, and accelerated inferencing at scale—bringing structure, context, and speed to data-intensive pipelines.

For companies forging ahead with AI, Infinia is the difference between stagnation and dominance. In a landscape where falling behind means lost market share and untapped data equates to missed millions, Infinia turns ambition into outsized returns—delivering operational efficiency, tangible business outcomes, and a clear path to lead the pack. It’s the foundation—and the intelligent engine—behind the AI-enabled enterprise.

Why AI Needs Data Intelligence to Thrive

AI has the potential to revolutionize industries, but without data intelligence, it is too expensive to sustain, and its full power remains untapped. Organizations have spent decades collecting massive amounts of multimodal data—structured and unstructured, spread across multiple physical locations, applications, hybrid cloud environments and now the edge. The challenge today is no longer just about having data, but about making it usable at AI speed and scale.

AI models rely on rapid access to the right data, but traditional data management methods can’t keep up with the complexity, performance demands, costs, and security risks associated with modern AI. Data intelligence bridges this gap—leveraging metadata to quickly access the essential information contained in your data — the actual knowledge, to streamline AI workflows, optimize infrastructure, and ensure seamless, scalable access to critical data.

The Four Critical Challenges Stalling AI Progress

• Complexity: AI workflows rely on diverse, dispersed datasets—structured and unstructured data, spanning clouds, data centers, and edge environments. Each has different formats, security rules, and processing needs, while legacy applications and fragmented AI tools add further complexity. Without unified data intelligence that can allow the enterprise to modernize without disrupting existing tools and processes, innovation slows, and accuracy suffers.
• Performance: AI success depends on speed, but high latency bottlenecks disrupt GPU utilization, slow training, and delay real-time inference. Inefficient data movement forces excessive infrastructure spending, driving up costs and reducing efficiency.
• Cost: The cost of AI is skyrocketing— not just in compute, but also in power consumption, storage, and cloud egress fees. Every inefficiency has a compounding effect, making AI projects financially unsustainable. Reducing data movement and optimizing resources is critical to achieving ROI.
• Secure & Reliable Scalability: AI must scale seamlessly while ensuring security and uptime, balancing AI adoption with strict compliance rules. Legacy architectures weren’t built for AI’s demands, leading to data silos, governance gaps, and increased risk as data volumes grow.

Why Legacy Infrastructure Can’t Keep Up

Legacy storage architectures—built for traditional analytics—struggle to keep up with AI. Hadoop, once a dominant big data platform, no longer has the performance needed for AI workflows, resulting in bottlenecks, high power costs, and extreme complexity. Legacy systems, tethered to rigid architectures and sequential processing, choke under the parallel, data-intensive demands of today’s AI, leaving GPUs and TPUs starved for data and driving up compute costs.

Their inability to handle the unstructured, multi-modal datasets powering modern models—like LLMs and generative AI—creates delays that stall innovation and erode market position. Worse, outdated storage often requires costly overhauls or patchwork fixes, draining budgets while failing to deliver the real-time performance AI requires. Organizations relying on NFS-based or disaggregated architectures face scalability limits, complex workloads, and skyrocketing expenses.

The advent of large language models (LLMs) and multi-modal models has made it imperative for organizations to transition to platforms that are not just scalable and fast, but also capable of enabling revolutionary innovations. McKinsey reports that AI could potentially deliver additional global economic activity of around $13 trillion by 2030, underscoring the critical need for advanced AI infrastructure.

Organizations seeking to drive innovation and maintain a competitive edge must look to modern platforms purpose-built to support AI workloads.

A Data Intelligence Platform for the New AI Era

DDN Infinia is uniquely positioned to fill this gap. Built in collaboration with NVIDIA and xAI, DDN Infinia is a next-generation data intelligence platform designed to meet the unprecedented performance, scalability, and efficiency demands of AI. With native metadata intelligence and extreme low-latency data access, Infinia unifies structured and unstructured AI datasets across multi-cloud, on-prem, and edge environments—simplifying AI workflows, securing critical data, and unlocking the full potential of AI-driven insights.

With sub-millisecond latency and faster metadata-driven AI pipelines, it replaces outdated storage models with a streamlined, software-defined platform that can handle massive data volumes and high computational requirements of modern AI. Infinia supports real-time data analytics, instant AI inference, and seamless integration with NVIDIA-powered AI stacks—delivering 10x higher efficiencies than traditional file systems. It supports data starting at a few hundred terabytes and seamlessly scaling all the way to multi-exabytes.

Designed as a cloud-native, software-defined data intelligence platform, Infinia seamlessly bridges AI execution engines, data orchestration tools, and developer workflows. It supports native access to object, file, and block storage while integrating directly with AI frameworks like Spark, TensorFlow, and Ray. Infinia is designed to solve the four key challenges that enterprises face with AI today.

Reduce Complexity

• Unified AI Data Fabric: Unify multi-modal data across clouds, edge, and core.
• Seamless Integration: Avoid reformatting data and reconfiguration delays with native multi-protocol support.
• One Platform for AI: Centralize multiple tools for AI Data Analytics, Data Preparation, Model Loading, and Inference.

Accelerate Innovation

• GenAI & LLMs: Reduce training and deployment time, ensuring faster insights.
• GPU Optimization: Maximize GPU utilization by minimizing data movement.
• Real-Time AI: Support RAG-enabled indexing and ultra-low latency document retrieval for instant AI inferencing responses.

Lower Costs

• 10x Data Reduction: Use metadata-driven intelligence to minimize data movement and egress costs in cloud environments.
• Power Savings: Reduce power & cooling costs while operating at massive scale.
• NVIDIA’s internal tests show 30%+ GPU wait times due to slow data access. Infinia’s low latency accelerates data delivery to GPUs.

Provide Proven Reliability & Security

• Validated with NVIDIA & xAI: Start small and scale seamlessly as AI demands grow—proven reliability, stability, and availability at scale with 100K+ GPUs and top AI innovators.
• Security driven: Built-in secure multi-tenancy, encryption, fault-domain-aware erasure coding, and data protection ensure enterprise-grade reliability and security.
• Ensure optimal resource allocation: Meet the varying demands of GPUs, efficiency, and performance with ease.
• 100% Software Defined: Your infrastructure needs may change, and infinia future-proofs any environment by being able to run heterogeneously on X86, ARM, containerized, virtual, cloud environments. Infinia is optimized to work with any NVMe flash, whether TLC, QLC or PLC to allow for cost optimization depending on your needs.

DDN Infinia: A Modern Architecture to Shape the New Age of Data Intelligence

DDN Infinia is architected from the ground up as a 100% software-defined, containerized, cloud-native data intelligence platform designed to support modern AI workflows, ensuring organizations can seamlessly manage, query, and optimize their data at an unprecedented scale.

Seamless Integration Across AI Workflows

Infinia bridges the gap between AI execution engines, data storage, and AI data acceleration libraries, creating an intelligent, highly adaptable data fabric.

Native Data Access & Protocol Support:

• Protocol Support: Supports native data access across Object, File, and Block protocols—eliminating the need for unnecessary data transformations. The short term Infinia roadmap includes enhanced file system support, application-native SDKs, and expanded APIs for automation and analytics integration—prioritized to meet strategic customer needs.
• Data Conversion Elimination: Eliminates the need for complex data conversion, enabling organizations to use SQL commands to query and process data instantly from distributed datasets.
• Cloud and Hybrid Readiness: Cloud-native & hybrid-ready – seamlessly integrates across public clouds, multi-cloud environments, on-premises data centers, and edge computing nodes.

Optimized AI Data Processing & Acceleration:

• AI Framework Integration: Integrates seamlessly with AI frameworks such as Spark, TensorFlow, PyTorch, Ray, and MosaicML, ensuring AI models can be trained and served efficiently without requiring new tools or workflows.
• AI Execution and Data Access Acceleration: Boosts AI execution with engines like Spark, enhances query performance with Trino, and accelerates data warehouse access, delivering high-speed, native- format data to slash latency.
• AI Deployment and Inference Speedup: SDK acceleration libraries speed up AI deployment and inference by bypassing multiple protocol layers and reducing indexing times by 3.3x in AWS and 4-5x on-prem with 10x lower latency for AI workloads.

Metadata-Driven Intelligence for AI Search & Discovery

One of Infinia’s most significant differentiators is its unlimited metadata scalability. Unlike traditional storage systems with limited metadata capabilities, Infinia’s fully distributed metadata eliminates legacy constraints, unlocking real-time search, discovery, and optimization for AI-driven workloads.

Metadata-Rich AI Workflows:

• Real-time Data Tagging and Indexing: AI applications require real-time data tagging and indexing for efficient data discovery.
• Scalable Metadata Tagging: Infinia supports millions of metadata tags per object, enabling faster, more precise search, retrieval, and inference optimization.
• Improved AI Pre-processing: Drastically improves AI pre-processing by streamlining data classification and retrieval-augmented generation (RAG).

Native Multi-Tenancy & Intelligent Resource Allocation

Infinia eliminates traditional bottlenecks in AI deployment by natively supporting multi-tenant architectures with built-in quality of service (QoS) controls, ensuring AI applications run smoothly without resource contention. Unlike other storage systems where multi-tenancy is an afterthought, Infinia was designed from the ground up to support secure, scalable, and dynamic tenant management designed to support cloud providers and other hyperscalers, supporting tens of thousands of clients and tenants.

Multi-Tenancy Optimized for AI at Scale:

• Dynamic SLA Enforcement: Quickly and easily ensure high-priority AI workloads receive top-tier performance guarantees while allowing secondary tasks to utilize idle resources without manual intervention.
• Isolated Resource Management: Guarantees secure, predictable data performance across multiple users and AI teams.
• Elastic Scaling: Enables organizations to start small with a few terabytes and seamlessly scale to exabyte-scale AI workloads.

Infinia’s Data Ocean

Built on a scalable and reliable key-value (KV) store, Infinia simplifies data integration and serves as an ideal source for all structured and unstructured data, including existing data lakes.

The KV Cache stores transformer model attention data, enabling faster token generation and inference. Infinia serves KV Cache with sub-millisecond latency, avoiding costly recompute cycles. DDN Infinia is engineered to serve KV Cache at sub-millisecond latency, ensuring fast, efficient reuse of attention data. This reduces GPU idle time, improves real-time response accuracy, and lowers inference costs— outperforming traditional file systems that are not optimized for dynamic, high-speed KV access.

Core advantages include:

• Efficient Data Reduction: Always on compression and wide strip erasure coding reduce data and help lift usable capacity.
• Massive Scalability: Infinia’s novel KV store implementation enables scaling from small to large in a simplified manner. The KV store scales effectively to handle large datasets in the exabyte range.
• Query Efficiency: Infinia’s built-in distributed SQL service enables targeted querying of data subsets without the inefficiencies of full data traversal.
• Dynamic Data Layout: Infinia automatically optimizes the data layout for the size of the data objects coming in and ensures a balanced use of all system resources for accessing the data.

Infinia Event Engine & Analytics Accelerator

As AI workloads scale, managing and triggering intelligent data operations in real-time becomes essential. Infinia enables this through its event engine and analytics accelerator.

DDN Infinia fundamentally changes how data can be managed and accelerated within AI workflows. By integrating a distributed SQL query engine and a scalable event engine, Infinia introduces powerful new ways to automate and optimize data processing—dramatically improving efficiency, accuracy, and speed.

The Infinia Event Engine enables event-driven automation at scale. Any data-related event can be used as a trigger, powering intelligent workflow enhancements such as:

• Automatic Metadata Tagging: Automatically invoking a tagging engine during ingest to enrich data with context-aware metadata.
• Automated Metadata Population: Auto-populating metadata (e.g., project name, owner, usage policies) when datasets are provisioned to a tenant.
• Efficient Metadata Updates: Offloading metadata updates by issuing a single SQL command instead of performing costly read-modify-write cycles.

Combined with Infinia’s distributed SQL query engine, these capabilities turn metadata and data management into real-time, intelligent operations.

In addition, Infinia includes SDKs that integrate natively with popular AI and data frameworks—delivering data access-as-code. This allows developer tenants to interact directly with data through familiar APIs and tools, without needing to understand storage interfaces or low-level protocols. The result: frictionless, scalable workflows optimized for developer productivity and AI performance.

Built-In Security and Resilience from Enterprise AI to Hyperscalers

DDN Infinia delivers the security, governance, and reliability needed to support AI at scale—ensuring enterprises can innovate with confidence while safeguarding their most valuable assets, their data.

• Zero-Trust Access Controls: Role-based access control ensures only authorized users and applications can access sensitive AI datasets.
• End-to-End Encryption: Data is always encrypted in transit and at rest, securing AI models and training data against breaches.
• Comprehensive Audit Logging: Every action—administrative and data access—is tracked, providing full visibility for compliance and security monitoring. 99.999% Uptime: Fault-domain-aware erasure coding, data replication, and snapshot capabilities eliminate unplanned downtime risks.
• Continuous Data Integrity and Availability: AI workloads remain operational even in the event of hardware failures, ensuring uninterrupted model training and inference with advanced redundancy and data protection strategies such as fault-domain aware variable network erasure coding, data replication, and snapshots.
• Resilient Multi-Tenant AI Workloads: Dynamic resource allocation prevents noisy neighbor issues, maintaining consistent AI performance across tenants.

Simplify Deployment for Every Stage of Your AI Journey

DDN Infinia is designed to meet enterprises wherever they are on their AI journey—whether starting with a small proof of concept or scaling to support production-grade, multi-petabyte workloads. Its flexible, hardware aware architecture enables seamless deployment at any scale, from a handful of nodes to some of the largest AI infrastructure environments in the world.

In production today, DDN customers are running clusters of over 1,000 Infinia nodes, with configurations exceeding 100 nodes per cluster. At the same time, smaller deployments—such as a 10- or 20-node system—can be stood up quickly on premises or through the Google Cloud Marketplace and expanded incrementally as data and performance needs grow, on premises, in the cloud, or with hybrid configurations.

In one customer deployment, a 120-node Infinia cluster was operational and ready to handle I/O intensive applications in under 10 minutes. Infinia automates the provisioning process, performing self-checks to validate hardware health and identify any components—such as slow drives or faulty network links—for preemptive maintenance.

Its software-defined networking layer allows links to be added or reconfigured on the fly, improving performance or redundancy without requiring restarts. Nodes can be introduced or decommissioned dynamically, ensuring high availability and continuous uptime.

Policy-based data placement and resilience features give organizations fine-grained control over data durability and access—whether protecting across racks, data halls, or entire facilities. From small-scale projects to mission-critical AI infrastructure, Infinia delivers consistent simplicity, performance, and reliability.

Accelerating AI Implementation

Whether you want to jump start your first AI models or you are managing petabytes of production data, DDN Infinia delivers the performance, flexibility, and intelligence needed to drive real business impact. Infinia serves as the cornerstone of DDN’s Data Intelligence Platform. Combined with EXAScaler®, it delivers a comprehensive solution offering simplicity and acceleration for end-to-end AI workflows and the world’s most powerful model training engine. This platform reduces complexity, lowers costs, accelerates innovation, maximizes training efficiency, and ensures proven reliability and security.

In the near future DDN now will extends these capabilities with integration services that kick start AI initiatives for key industries and the critical Inference use case, significantly reducing time to market. Two innovative offerings—DDN IndustrySync and DDN Inferno—enable rapid deployment and optimization of AI services across the enterprise.

DDN IndustrySync provides industry-specific, end-to-end AI solutions for Financial Services, Life Sciences, and Autonomous Driving. By integrating Infinia’s data intelligence capabilities with NVIDIA DGX systems and major cloud providers, IndustrySync delivers turnkey workflows with up to 10x faster processing in key areas. This translates to substantial business value, potentially exceeding $100M in annual returns for enterprises.

DDN Inferno tackles the critical inference phase of AI deployment. Built on Infinia, this acceleration application provides a turnkey integrated system for inference, achieving industry-first sub-millisecond response times and reduces latency by up to 10x for real-time AI applications. By optimizing GPU utilization to 99% during inference workloads, Inferno delivers exceptional cost efficiency demonstrating a 12x advantage over AWS S3-based inference stacks in early testing.

Together, these solutions underscore DDN’s commitment to simplifying AI implementation and accelerating time to market and ROI for the enterprise.

Unleash Your AI Potential with DDN Infinia

With support for deployments of all sizes, Infinia empowers organizations to start small, move fast, and scale seamlessly—eliminating data bottlenecks, reducing operational complexity, and accelerating time to AI-driven outcomes. Its intelligent automation and deep integration across the AI pipeline help reduce infrastructure overhead while maximizing return on investment.

Schedule your Infinia demo to see how this cutting-edge data intelligence platform can support your next-generation AI initiatives. Let’s start building your AI stack together.