Post by Harald Seipp

🚀 𝗦𝘂𝗽𝗲𝗿𝗰𝗵𝗮𝗿𝗴𝗶𝗻𝗴 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗜𝗕𝗠 𝘄𝗮𝘁𝘀𝗼𝗻𝘅.𝗮𝗶 + 𝗜𝗕𝗠 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗦𝗰𝗮𝗹𝗲 AI adoption is accelerating—but many organizations quickly discover that 𝘥𝘢𝘵𝘢 and 𝘴𝘵𝘰𝘳𝘢𝘨𝘦 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 become major bottlenecks. Large models, GPU clusters, and RAG pipelines demand fast, scalable, and secure access to massive datasets. IBM has published a new Redpaper (https://lnkd.in/ebXSJ_qq) showing how 𝗜𝗕𝗠 𝘄𝗮𝘁𝘀𝗼𝗻𝘅.𝗮𝗶 and 𝗜𝗕𝗠 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗦𝗰𝗮𝗹𝗲 work together to create a next‑generation enterprise AI platform. The value becomes clear when you combine both technologies: 🔥 𝗪𝗵𝘆 𝗜𝗕𝗠 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗦𝗰𝗮𝗹𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗔𝗜 • High‑throughput, massively parallel storage designed to keep GPU clusters fully utilized • Multi‑protocol access (POSIX, NFS, SMB, S3) without data duplication • Global caching of cloud/on‑prem data using AFM to cut latency and egress costs • Enterprise resiliency, snapshots, encryption, and seamless tiering 🤖 𝗪𝗵𝘆 𝗜𝗕𝗠 𝘄𝗮𝘁𝘀𝗼𝗻𝘅.𝗮𝗶 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗱𝗲𝗮𝗹 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲 • Full lifecycle support for training, tuning, and running Granite foundation models • Tools like Prompt Lab and Jupyter notebooks for fast development • Integration with watsonx.data for Lakehouse analytics, Iceberg catalogs, Spark, and vector databases • Enterprise governance and hybrid cloud flexibility built on Red Hat OpenShift 💡 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗼𝘄𝗲𝗿 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗯𝗼𝘁𝗵 Together, IBM watsonx.ai + IBM Storage Scale enable: • High‑performance training and inference with consistent GPU utilization • Fast, scalable RAG pipelines with Milvus vector DB backed by Storage Scale S3 • Hybrid cloud acceleration using cached cloud object stores • Multi‑tenant, enterprise‑secure AI architectures (including role‑based content filtering) • Zero data copies—one global data platform for all AI workloads The Redpaper also walks through practical examples, including Docling‑based document extraction, OpenRAG Benchmark ingestion, domain‑specific scientific RAG, and secure multi‑role RAG pipelines. 📘 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻‑𝗴𝗿𝗮𝗱𝗲 𝗔𝗜, 𝘁𝗵𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝘄𝗼𝗿𝘁𝗵 𝗮 𝗹𝗼𝗼𝗸. It shows how organizations can turn fragmented data into a unified, high‑speed AI ecosystem that’s ready for today’s and tomorrow’s generative AI workloads. Read the full paper at https://lnkd.in/ebXSJ_qq FYI Dr.-Ing. Qais Noorshams Chinmaya Mishra Sailendu Patra Dietmar Fischer Kedar Karmarkar Mathias Defiebre Alexander Saupp Florin Manaila Carsten Laux Amelia Forbes Matthias Biniok #ibm #storage #storagescale #watsonx #genai #rag #openshift