DDN Infinia Expands to Power Inference, Intelligent Data Lakes, and Multi-Protocol AI Workloads
DDN, Monday, April 6th, 2026
Why 2026 is the year storage architecture has to change - and why Infinia is the platform leading that change.
The conversation about AI infrastructure is changing.
For the past several years, the dominant question was: can your storage keep up with training? Sustained throughput, checkpoint speed, metadata at scale - the storage layer's job was to keep GPU clusters fed without becoming the bottleneck. DDN built Infinia to answer that question, and the platform has done exactly that, validated today at scales exceeding 100,000 GPUs across some of the world's largest AI deployments.
But the question the market is asking in 2026 is different, and more interesting.
It is no longer just about training. AI factories are now running continuous, multi-stage pipelines: inference serving millions of requests in parallel with training, RAG pipelines querying the same data lake that ingestion pipelines are still filling, multi-protocol workloads from research and life sciences teams that need POSIX semantics alongside the object store. The infrastructure layer underneath all of this has to do something legacy architectures were never designed to do - serve all of these workloads simultaneously, on shared infrastructure, with hard isolation and consistent performance guarantees, at any scale.
This is the moment DDN has been building toward. And this release marks the moment Infinia steps fully into it.