Accelerating AI with Unified, Intelligent Storage
AI innovation is no longer gated by model performance — it’s constrained by data. The shift to Retrieval-Augmented Generation (RAG) workflows places enormous pressure on how enterprises store, access, and operationalize data across clouds, edge, and core. This paper explores how DDN Infinia transforms object storage from a static archive into a real-time data engine — delivering the low-latency search, rich metadata handling, and seamless multi-modal data integration that RAG demands. Built for AI-scale performance and simplicity, DDN Infinia enables organizations to unlock faster insights, better decisions, and real business value from their AI investments.
Relevancy at Speed: How DDN Infinia Powers RAG Workflows
Relevancy is the cornerstone of effective AI solutions. Large Language Models (LLMs) like Gemini, Grok, and OpenAI are not pre-trained on proprietary or real-time data, limiting their ability to deliver accurate, domain-specific outputs. Retrieval-Augmented Generation (RAG) addresses this by enhancing LLM outputs with up-to-date, contextually relevant information.
DDN Infinia, paired with NVIDIA’s GPU-accelerated cuVS library, can also serve as a high-performance, standalone vector store for RAG. This solution integrates seamlessly with leading LLMs, offering unmatched speed and scalability. In contrast, traditional 3rd party vector databases often rely on non-GPU-accelerated systems, introducing network latency and scaling costs that degrade performance, especially in multi-cloud environments.
Key Benefits of DDN Infinia + cuVS
- Replaces third-party vector databases entirely
- GPU-accelerated for low-latency, high-throughput workloads
- 4–18x faster indexing; up to 100x faster search
- Delivers 600x faster object listing compared to AWS S3
DDN Infinia + cuVS: Accelerated RAG Performance
DDN Infinia, configured as a vector store, leverages NVIDIA’s cuVS library for GPU-optimized similarity search (e.g., IVF-PQ, CAGRA algorithms). This combination delivers exceptional performance for RAG pipelines, making it ideal for industries requiring rapid, accurate data retrieval, such as autonomous vehicles, pharmaceuticals, and financial services.
Performance Comparison
For a 10M document vector database, DDN Infinia + cuVS significantly outperforms a 3rd party vector database:
Query Latency
- Infinia + cuVS: 1.1–6ms (search: 1–5ms, network: 0.1–1ms)
- 3rd Party VectorDB: 120–400ms (search: 100–300ms, network: 20–100ms)
Speedup: 20–100x faster retrieval
End-to-End RAG Latency
- Infinia + cuVS: 0.551–2.106s (retrieval: 1.1–6ms, embedding: 50–100ms, generation: 0.5–2s)
- 3rd Party VectorDB: 0.670–2.500 s (retrieval: 120–400ms, embedding: 50–100ms, generation: 0.5–2s)
Speedup: 10–25% faster end-to-end
Indexing Speed
- Infinia + cuVS: 10–30min for 10M vectors, updates at 10,000 vectors/s
- 3rd Party VectorDB: 1–3hr for 10M vectors, updates at 1,000–5,000 vectors/s
Speedup: 4–18x faster indexing
Scalability and Integration
- Infinia + cuVS: Scales with GPU nodes (e.g., NVIDIA A100, L4), offering predictable performance for high-throughput workloads. Tightly integrated with GCP (Vertex AI, Cloud Run) for low-latency API calls.
- 3rd Party VectorDB: Auto-scales with a pay-as-you-go model but incurs higher costs and potential warmup delays under sudden load spikes. Potential cross-cloud latency (e.g., AWS to GCP) adds overhead.
*Based on internal DDN testing.
The Hidden Cost of Legacy Storage
Most enterprises don’t realize how much their legacy storage is costing them. Not just in dollars, but in missed opportunities—AI models that take too long to train, siloed data that can’t be tapped for insights, and customers who leave due to lagging digital experiences. The difference between success and irrelevance isn’t your AI strategy. It’s whether your data systems are built for AI at all.
Business Outcomes
Speed as a Differentiator
In AI, speed isn’t just a technical metric—it’s a business advantage. Faster model prep, retrieval, and response times mean quicker decisions, better outcomes, and competitive differentiation. Speed is critical in RAG pipelines, particularly for AI agents requiring real-time responses across various industries. Multiple RAG calls are often used to rank results, ensuring the most accurate data is passed to the LLM. Infinia + cuVS delivers query retrieval up to 100x faster than 3rd party vector databases, enabling transformative business outcomes.
Infinia Business Impact: Industry Outcomes & Economic Value
| Industry | AI-Driven Business Outcome |
Estimated Savings |
| Autonomous Vehicles | Fewer route failures; improved fleet utilization | $5M–$20M/year per 10,000 vehicles |
| Customer Support | Faster agent resolution; higher efficiency; reduced churn | $2M–$10M/year per 1,000 agents |
| Pharma R&D | Accelerated drug candidate discovery; more effective trials | $10M–$50M+ per drug pipeline phase |
| Manufacturing | Increased uptime; lower maintenance and repair costs | 8%+ downtime reduction, significant cost savings |
Why Faster is Better
- Simplify AI Data & Infrastructure Support:
Unify multi-modal data across clouds, edge, and core with seamless, multi-protocol support. - Accelerate AI Performance & Insights:
Reduce GenAI and LLM training times, maximize GPU efficiency, and enable real-time RAG inferencing with ultra-low latency. - Lower Costs & Improve Efficiency:
Achieve 10x data reduction, cut power and cooling costs, and eliminate GPU idle time with faster data delivery. - Ensure Enterprise-Grade Reliability & Flexibility:
Built-in security, multi-tenancy, and 100% software-defined flexibility for any environment.
From Data Chaos to Data Intelligence
DDN Infinia, powered by NVIDIA cuVS, redefines RAG performance, offering 20–100x faster retrieval, 10–25% faster end-to-end latency, and 4–18x faster indexing compared to 3rd party vector databases. This makes it the ideal choice for low-latency, high-throughput workloads in AI-driven applications across industries like autonomous vehicles, pharmaceuticals, financial services, and healthcare.
What’s Next
As AI becomes core to every industry’s growth strategy, the stakes are higher than ever. Organizations that can unify, govern, and accelerate their data will lead—those who can’t will struggle to keep up. DDN Infinia is more than a storage platform; it’s a shift in mindset. It brings order to chaos, insight to raw data, and agility to once-rigid systems. If you’re ready to move beyond artificial—to power real, tangible outcomes—then it’s time to rethink what your data platform can do.
