The End-to-End AI Software Data Solution for Best-in-Class AI and GPU ROI in Data Centers and Multiclouds – Any Data. Any Application. Any Industry. Anywhere.
DDN, a recognized leader in AI data intelligence, introduced DDN Infinia 2.0—a software data platform that delivers global AI data unification in data centers and multi-clouds. Built on a proven, infinitely scalable architecture, Infinia 2.0 solves core challenges in AI data analytics, model training, inference, and boosts GPU efficiency, and minimizes power consumption. Independent benchmark tests indicate up to 100X improvement in AI data acceleration and 10X gain in data center and cloud cost efficiency.
“Infinia 2.0 represents a paradigm shift in how enterprises and cloud providers gain value from AI while safely managing and optimizing AI data workloads,” said Alex Bouzari, CEO at DDN. “85 of the Fortune 500 businesses run their AI and HPC applications on DDN’s Data Intelligence Platform. With Infinia, we accelerate customers’ data analytics and AI frameworks with orders of magnitude faster model training and accurate real-time insights while optimizing GPU efficiency and power usage. Infinia integrates seamlessly into cloud-native ecosystems, delivers the safest multi-tenancy, and mixes enterprise and cloud AI ROI without disrupting existing IT infrastructures.”
Adding further perspective, Paul Bloch, Co-founder and President of DDN, commented, “Whether you’re a CXO of an enterprise looking to kickstart and accelerate your AI initiatives or a data scientist seeking to supercharge AI applications with the most performant and scalable data fabric, DDN’s Infinia is the only answer. Our platform has already been deployed at some of the world’s largest AI factories and cloud environments, proving its capability to support mission-critical AI operations at scale.”
What Leaders Are Saying
“Data is the lifeblood of AI, which is why enterprises need integrated, full-stack systems and software to drive modern applications,” said Charlie Boyle, vice president of DGX platforms at NVIDIA. “Combined with NVIDIA accelerated computing and enterprise software, platforms like DDN Infinia 2.0 provide businesses the infrastructure they need to put their data to work.”
“By combining DDN’s data intelligence platform Infinia 2.0 with Supermicro’s cutting-edge server workload-optimized solutions, the companies collaborated on one of the world’s largest AI data centers,” said Charles Liang, president and CEO at Supermicro. “The ease of the Infinia deployment was due to the close working relationship between Supermicro and DDN.”
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Why Infinia 2.0
Infinia 2.0 simplifies complexity, accelerates performance, and secures data at any scale while reducing power consumption and cooling needs in data centers by 10x. By unifying data operations and automating AI workflows, Infinia empowers organizations to unlock the full potential of their data, driving AI transformation, and success.
“AI workloads require real-time data intelligence that eliminates bottlenecks, accelerates workflows, and scales seamlessly in complex model listing, pre- and post-training, RAG, Agentic AI, Multimodal environments and inference,” said Sven Oehme, CTO at DDN. “Infinia 2.0 was designed to maximize AI value in these areas, while also delivering real-time data services, highly efficient multi-tenancy, intelligent automation, and a very powerful AI-native architecture. This is the future of AI data enablement.”
Infinia 2.0: the AI Data Intelligence Which Maximize Enterprise and Cloud Provider Value
Infinia 2.0 addresses AI’s biggest challenges—complexity, performance, cost, and security— by delivering a unified data platform that seamlessly integrates AI inference, data analytics, and model preparation across core, cloud, and edge environments. It empowers enterprises to eliminate bottlenecks, maximize GPU efficiency, and enhance operational efficiency while ensuring data security and reliability at any scale. Core tenets of Infinia 2.0 include:
1. Dynamic Data Services & Workflow Acceleration
Infinia 2.0 simplifies and boosts data services with an architecture purpose-built to optimize AI workflows:
Real-time AI data pipelines accelerate AI/ML training, inference, and generative AI, minimizing latency for rapid insights.
Event-driven data movement automates workflows, ensuring data is always in the right place at the right time.
Secure, multi-tenant environments scale AI efficiently and cost-effectively while maintaining strict data security and isolation.
Fully software-defined & hardware-agnostic architecture maximizes infrastructure flexibility, optimizing performance using existing systems.
Optimized for scalable AI and proven in real-world data center and cloud deployments from 10 to 100,000+ GPUs for optimal efficiency and cost savings at any scale, to maximize GPU utilization.
2. Unified AI Data Intelligence Platform
The Data Ocean in Infinia 2.0 provides a global view of distributed datasets, simplifying AI data preparation, analytics, and inference:
Unified platform for any application provides a single platform for AI Data Analytics, Preparation, Model Loading, and Inference, reducing complexity by eliminating the need for multiple tools, data platforms and infrastructure.
Supports any data, anywhere; such as structured, semi-structured, and unstructured data, enabling actionable insights without duplication.
Seamless integrations with NVIDIA NeMo, NVIDIA NIM microservices, Trino, Apache Spark, TensorFlow, PyTorch, and other AI frameworks to accelerate AI applications.
Multi-protocol data access supports object, block, and other protocols, enhancing data management flexibility.
3. Unrivaled Performance & Efficiency
Infinia 2.0 is built to supercharge AI applications:
100x faster metadata processing, enabling ultra-fast AI processing.
100x faster object lists per second than popular clouds, supporting AI operations at scale.
10x faster AI workloads with 100x better efficiency than popular open source data frameworks.
TB’s/sec bandwidth & sub-millisecond latency outperform popular cloud instances by 10x, delivering unprecedented performance.
25x faster querying for AI model training and inference, accelerating the entire AI lifecycle.
4. Sustainable, Cost-Effective AI
Infinia 2.0 reduces costs while boosting efficiency:
10x reduction in power and cooling, driving sustainable AI operations.
Up to 10x always-on data reduction capabilities to maximize your investment
Supports 100PB in a single rack at ¼ the footprint of competing solutions, maximizing density and scalability.
Intelligent, metadata-driven automation minimizes data movement and egress costs, ensuring cost-effective data operations.
Can offload networking and encryption to NVIDIA BlueField DPUs for even higher power and cooling efficiencies.
5. Proven, Trusted Reliability & Security at Any Scale
Infinia 2.0 offers unmatched reliability and security:
99.999%+ uptime, advanced end-to-end encryption, and certificate-based access for secure and always-available data environments.
Fault-tolerant network erasure coding and automated QoS ensure data integrity and resource isolation.
Scales from terabytes (TB) to exabytes (EB), supporting up to 100,000+ GPUs and 1 million simultaneous clients in a single deployment, enabling large-scale AI innovation.
These tenets create a platform optimized for AI operations by maximizing performance, enhancing data security, and reducing complexity and cost, empowering organizations to unlock the full potential of their data and drive AI innovation and success.
Built for the Future of AI
With over 25 years of expertise in successfully designing and bringing to market world-leading AI and high-performance data solutions, DDN continues to push the boundaries of AI and data intelligence innovation. Built to maximize AI value for enterprises, cloud providers, AI factories, sovereign AI as well as global AI leaders NVIDIA and xAI, Infinia is the definitive AI data platform, future-proofing enterprises to safely supercharge their AI initiatives in data centers and multi-clouds, and achieve highest success for the next generation of GenAI, LLMs, and Retrieval-Augmented Generation (RAG) workloads.
Source: Businesswire
