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VDURA gallops into GPU-using Neocloud and enterprise market VDURA says its latest and generally available v12.0 software release turns it into a multi-tenant, API-driven storage service for GPU clouds and AI factories. High-performance parallel filesystem hardware and PanFS software supplier Panasas rebranded itself to VDURA and went energetically into the software-led, enterprise AI storage market in 2024, with new CEO Ken Claffey. The PanFS v11 release was an AI-era rebuild of the prior v10 software, branded as the VDURA Data Platform (VDP) and introduced VeLO (Velocity Layered Operations) and a VPOD (Virtualized Protected Object Device) concept. VDP is deployed as discrete microservices, simplifying deployment across thousands of nodes and helping ensure linear performance scalability with an infinitely expandable global namespace. Now v12 continues the AI focus for large GPU users and also develops its HPC attributes. Claffey said: "Neocloud and AI factory operators told us exactly what they need from storage: keep the GPUs fed, isolate the tenants, automate everything, expand capacity for cold data without a second system, and move that data back to flash the moment it warms up for extended context. V12 is that list, shipped. It is the same mixed-fleet, software-defined model the hyperscalers run inside their own clouds, delivered on Supermicro systems our customers already buy. Every watt and every rack unit we give back is another GPU the operator can put into service.” V12 has a HYDRA (High-performance, Yield-optimized, Distributed, Resilient Architecture) design and is aimed at multi-tenant, GPU cloud infrastructures, delivering; - Multi-tenancy - per-tenant quality of service, namespaces, encryption keys and VLAN isolation on one shared fleet, so a provider can carve a single storage pool into hard-walled tenant services with capacity and performance guarantees. - API-first automation - REST APIs, Kubernetes CSI and infrastructure-as-code tenant provisioning, so storage is deployed, provisioned and billed through the same pipelines as the rest of the GPU cloud. - Context-Aware Tiering - data lands on the right media automatically as access patterns shift between training and inference. Roughly 90 percent of files stay on flash while roughly 90 percent of capacity settles on HDD, in one platform with no stub files, no rehydration steps and no manual tuning. - Persistent context for inference - a KV cache that outlives the pod. Sessions resume instead of prefilling again, delivering faster first tokens and more concurrent users on the same GPUs, at flash cost rather than recompute cost. - RDMA data paths - direct GPU-to-storage transfers with the CPU out of the path. The DirectFlow parallel client takes roughly 191 MB of DRAM and zero cores from the GPU node. - Elastic Metadata Engine - VeLO metadata acceleration of up to 20x improvement, 225,000 creates and deletes per second per Director, and billions of metadata operations per second in aggregate. - File and S3 in one platform - an S3 object is a file in the volume, not a copy of one. No staging copies between ingest, training, inference and archive. - Snapshots and SMR HDD optimization - instantaneous, space-efficient snapshots for checkpoints and operational recovery, and SMR (Shingled Magnetic Recording) disk drives unlocking 25 to 30% more capacity per rack. - End-to-end encryption - AES-256 at rest and in flight, with KMIP key management per tenant. - Self-healing resiliency and VDURA Sentinel - failure domains as small as a single VPOD, no manual rebuilds and no downtime windows, backed by VDURA Sentinel proactive support that opens the service request, with the diagnosis attached, before the customer sees a fault. Back in June last year, Claffey said VDURA should be classed alongside DDN, VAST Data, and WEKA as an extreme high-performing and reliable data store for modern AI and traditional HPC workloads. (We include Everpure and now NetApp in that group.) The V12 software, first announced in November 2025, is a proof point for his belief. VDURA Data Platform V12 is generally available today for all V5000 class systems and as an upgrade for V11 customers. It ships as a qualified solution on Supermicro Building Block Solutions, scaling from 8 to 100,000 GPUs on one software stack. V12 will be shown at the Ai Everything event in Abu Dhabi, 6–7 October at ADNEC Centre, Booth H3-D45. Download a VDP V12 white paper here. Bootnote The V12 Supermicro config uses the Supermicro AS-1116CS-TN, a 1U system with a single AMD EPYC 9005 series processor and 12 NVMe bays, as both the VeLO Director node and the all-flash F-Node, alongside the AS-2015HS-TNR hybrid storage node and the CSE-947HE2C 4U 90-bay JBOD for the mixed-fleet data plane. Every node connects with RDMA straight to the GPU nodes and there is no dedicated back-end storage fabric. VDP v12 clusters grow online from three nodes to thousands, and flash share is a dial rather than a fork, meaning the flash-to-HDD ratio is a continuous, online setting you can change by adding all-flash nodes, hybrid nodes, or HDD expansion shelves independently. You do not have to pick one architecture and then “fork” into a separate all-flash system plus a separate capacity/object system (two stacks, two namespaces, data copies). One control plane and one data plane span both media; you simply turn the mix up or down as needs or prices change. VDURA says a single VDP system at roughly 20 PB usable capacity spans a 35x performance range, from a capacity-optimized mixed fleet at 2 percent flash and 18 kW to an all-flash configuration at 1,000 MB/s per TB, so operators size storage to the workload instead of standing up a second system when the workload changes. The result is 2x+ performance per watt and more than 60 percent lower total cost of ownership than competitive architectures at the same feed rate, which returns power, rack space and capital to the operator for more GPUs.
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