🎥 See DDN in action powering real-world innovation in life sciences! At Scripps Research, AI and high-performance computing are transforming how researchers tackle some of the world’s most complex biomedical challenges. From drug discovery to disease modeling, speed and scale are everything. 🚀 That’s where DDN comes in. Our Data Intelligence Platform delivers the performance, scalability, and efficiency needed to accelerate GenAI, RAG, and LLM workloads—turning massive data into meaningful discoveries. Watch how Scripps is leveraging DDN to drive the future of biomedical AI. 👇 #GenAI #AIinHealthcare #LifeSciences #DataIntelligence #HPC #BiomedicalResearch #DDN #AIInfrastructure #RAG #LLM #ScrippsResearch

🔍 The Hidden Bottleneck in AI Infrastructure? It’s Not What You Think. Too many AI initiatives stall because of a silent killer: storage that can’t keep up. According to the Gartner® Hype Cycle™ for Storage Technologies, 2025, the spotlight is finally on what really matters: 📌 Storage Platforms for Generative AI 📌 Open-Source Storage Software 🚀 DDN is recognized as a Sample Vendor in both categories – a reflection of how we’re enabling AI to perform at scale, in production, and across industries. Why does this matter? ✅ Real-time data access for RAG & LLMs ✅ Consistent, intelligent performance across cloud, edge & core ✅ Open-source power through leadership in Lustre ✅ 3x AI throughput, 70% faster fraud detection, and $M+ in GPU efficiency gains AI doesn’t just need more compute. It needs smarter infrastructure. And that starts with the right data layer. 👉 Learn why Gartner sees these trends as key innovation triggers — and why DDN is leading the charge: #AIInfrastructure #GenerativeAI #RAG #StorageForAI #DDN #GartnerHypeCycle #EXAScaler #Infinia #DataIntelligence #OpenSourceStorage #AIReadyStorage #GPUAcceleration

🚨 AI inferencing isn’t an afterthought — it’s the front line. In his latest piece for Forbes, DDN CTO Sven Oehme breaks down five costly myths about AI inference that are quietly draining performance, budget, and business value. From underpowered storage to misaligned cloud strategies, most organizations are architecting for training — and ignoring the phase where AI actually meets the real world. 💡 The takeaway? Inference happens millions of times a day. If your infrastructure isn’t built for that scale, your AI strategy is already behind. 🧠 Read the full article for practical insights on: - Optimizing GPU and storage for real-time AI - Architecting edge and hybrid deployments - Avoiding technical debt before it’s too late 🔗 Read: #AIInfrastructure #InferencingMatters #EdgeAI #HybridAI #DataPerformance #DDN #ForbesTechCouncil #AIinProduction #RealTimeAI

💸 In financial services, milliseconds matter—and underutilized GPUs can cost millions. As firms scale AI for fraud detection, risk modeling, and algorithmic trading, one silent bottleneck is slowing everything down: data movement. 🚫 The issue isn’t your compute power—it’s your data infrastructure. Legacy storage can’t keep up with modern AI workloads, leading to idle GPUs and missed opportunities. ✅ DDN’s Data Intelligence Platform solves this with: 🔹 25x lower latency vs. traditional cloud storage 🔹 Scalable metadata for audit-ready model development 🔹 Automated pipelines to keep GPUs busy and business moving From LLM training to real-time risk scoring, leading financial institutions trust DDN to keep their AI pipelines fast, efficient, and fully utilized—across cloud, core, and edge. 📊 Optimized GPU utilization = real ROI and competitive edge. 🔍 Read how DDN helps financial firms cut AI costs and boost performance: #AI #FinancialServices #DataIntelligence #GPU #AIInfrastructure #HPC #RiskModeling #FraudDetection #Fintech #DDN