DDN storage in India: feed your GPUs, not your wait time.
The DDN storage desk at Sirius Star sizes EXAScaler and AI400X2 to your real GPU and HPC workload. You get a sized quote in 8 working hours, racked in your own datacentre.
· India’s IT & business services market reached USD 254 billion in FY24 and is projected to cross USD 350 billion by 2026, per India Brand Equity Foundation.
· Under the DPDP Act 2023, Indian businesses must keep personal data in India unless cross-border transfer is to a notified country — Sirius Star configures every deployment for DPDP compliance by default.
· Sirius Star is a Microsoft Partner with a cloud engineer on payroll, founded 2009 in Navi Mumbai, serving 200+ Indian enterprises across Cloud, Secure Data Guard, Device Lifecycle Management, Hardware, and Corporate Tech Gifting.
The DDN storage desk at Sirius Star sizes EXAScaler and AI400X2 to your real GPU and HPC workload. You get a sized quote in 8 working hours, racked in your own datacentre.
What is DDN storage, and who is it for?
DDN, short for DataDirect Networks, builds high-throughput storage for the heaviest data jobs in the building. If you run a GPU cluster, an HPC grid, a render farm or a large AI training pipeline, this is the storage tier that keeps those expensive processors fed instead of idle.
The core idea is a parallel filesystem. Many compute nodes read and write the same dataset at once, so the storage has to deliver in parallel rather than one stream at a time. DDN does this with EXAScaler, an engine built on the open-source Lustre filesystem and hardened for production. It powers a large share of the world’s fastest supercomputers, and the same design now sits behind enterprise AI factories.
Sirius Star supplies DDN in India end to end. We size, import, rack and tune the appliances, then support them after go-live. You deal with one desk in Navi Mumbai, not a chain of overseas tickets. For the GPU servers in front of the array, see our NVIDIA GPU and Supermicro GPU server pages.
What does DDN cost in 2026?
EXAScaler parallel filesystem
EXAScaler is the engine. A Lustre-based parallel filesystem, tuned by DDN for reliability and speed, it lets a whole rack of GPUs hit the same dataset without queueing. We size it to the effective throughput your jobs need, not a brochure peak. It sits in front of rack servers and GPU nodes.
From ₹40 lakh indicative, sized on review
AI400X2 and AI400X2 Turbo
The appliance that serves EXAScaler. NVMe flash from 30 TB to 500 TB, expandable past 5 PB in hybrid, on InfiniBand or Ethernet. Validated for NVIDIA DGX BasePOD and DGX SuperPOD, so the reference architecture is done for you.
Infinia data platform
DDN’s software-defined platform for AI data lakes. Infinia 2.1 and the AI400X3, shown at ISC 2025, serve object and file together at large scale. Useful when the bottleneck is finding and serving data, not raw speed.
GPUDirect Storage and NVIDIA
Data moves straight from the array into GPU memory, skipping the host CPU. On a validated DGX SuperPOD, Turbo appliances reach close to full network utilisation per DGX B200. The differentiator: the GPUs wait less.
SFA block and WOS object
Not every job is a GPU job. SFA delivers high-IOPS block for mixed HPC and databases, while WOS gives you an object tier for durable archive. One vendor across hot and cold data.
Tintri by DDN
For virtualised and database estates rather than GPU farms, Tintri gives per-VM visibility and auto-tuning. It is the practical DDN answer when the workload is VMware and SQL, not model training.
Sized, racked and supported by Sirius
The sixth piece is us. We turn a capacity sheet into a landed configuration in 8 working hours and tune it in your datacentre. Lead time is typically 6 to 8 weeks from a confirmed PO.
A GPU cluster waiting on storage is the most expensive idle hardware in your datacentre. When training data also holds personal records, ₹250 crore is the DPDP Act penalty ceiling per breach, per the Ministry of Electronics and IT, and incident timelines follow CERT-In rules.
We have seen a GPU pilot run at half its budgeted utilisation because the storage tier could not keep the nodes fed. The review that catches this is free. The wasted GPU hours were not.
How a DDN rollout actually runs
Free 24-hour workload review
Send the job profile: how many GPUs, what frameworks, dataset size and read pattern. We translate that into a real throughput and capacity target, so you buy for the workload rather than a round number.
Sized quote in 8 hours
Inside 8 working hours you get the appliance count, effective capacity, network plan, landed price and lead time. No vague ranges. One configuration you can take to the CFO.
Rack, tune and migrate
We install in your datacentre, tune EXAScaler to your nodes, and stage the data move so training is barely paused. The array pairs cleanly with Cisco UCS and your existing fabric.
Support that answers
After go-live you have a named desk in Navi Mumbai on a 4-hour response target, with firmware and health checks on a schedule.
Who should put DDN in the rack?
DDN earns its place when storage speed decides how fast your most expensive compute finishes. It is overkill for a file server and exactly right for a GPU farm.
AI and ML teams with GPU clusters Research and HPC grids Media, VFX and render farms BFSI quant and risk modelling Genomics and pharma pipelines
If your need is general enterprise storage rather than GPU throughput, NetApp ONTAP, Hitachi Vantara VSP or Infinidat are usually the better-value fit, and we will say so.
DDN EXAScaler vs WEKA vs VAST Data, the honest call
| Call | DDN EXAScaler | WEKA | VAST Data |
|---|---|---|---|
| Best at | Largest-scale GPU and HPC throughput | Software-defined, cloud-portable AI | Simple all-flash enterprise AI |
| Form | Appliance plus EXAScaler (Lustre) | Software on your servers or cloud | Disaggregated all-flash array |
| Proven scale | Top supercomputers, DGX SuperPOD | Strong on mixed and cloud GPU | Growing in enterprise AI |
| Best fit | Dedicated on-prem GPU factory | Teams wanting hardware freedom | Simpler, smaller AI estates |
For a dedicated on-prem GPU factory at large scale, DDN is the safe answer because the scale is already proven. If you want hardware freedom or a cloud-portable layer, WEKA deserves a look. For a simpler enterprise AI estate, VAST Data is often easier to run. We sell against all three honestly and recommend the one that fits your jobs.
DDN questions Indian buyers ask
Is DDN storage available in India, and who supplies it?
Yes. Sirius Star supplies DDN across India, from Navi Mumbai. We size, import, rack and support the appliances, so you have one local desk rather than an overseas support chain.
How much does a DDN AI400X2 cost?
Indicative 2026 pricing starts near ₹40 lakh for an entry AI400X2 and rises with NVMe capacity and appliance count. We give a firm, sized price in 8 working hours after a free workload review.
Is DDN certified for NVIDIA DGX SuperPOD?
Yes. The AI400X2 line is validated for NVIDIA DGX BasePOD and DGX SuperPOD, including recent DGX B200 systems, so the reference architecture is already proven rather than something you assemble yourself.
DDN EXAScaler or WEKA, which suits our GPU cluster?
For a large dedicated on-prem GPU factory, DDN EXAScaler is the proven choice. WEKA suits teams that want a software-defined, hardware-flexible or cloud-portable layer. We will recommend the honest fit for your jobs.
Does DDN data stay in India?
Yes. The array is a physical appliance in your own datacentre. The storage itself has no foreign cloud dependency, which keeps residency simple under DPDP and RBI rules.
What is DDN Infinia?
Infinia is DDN’s software-defined data platform for AI. It serves object and file together and manages metadata at large scale, which helps when the bottleneck is organising and serving data, not just raw read speed.
One desk. Sized, racked, tuned, supported.
Send the GPU count, frameworks and dataset size. Inside 8 working hours you get the configuration, effective throughput, landed price and lead time. The 24-hour review is free, and 200+ businesses already trust Sirius Star.
200+ businesses · Response within 8 hours · 17+ years in IT · Navi Mumbai
P.S. A research team near Pune almost bought twice the NVMe they needed. A short review showed their reads were sequential, so a smaller AI400X2 kept every GPU fed. The review costs nothing. The second shelf would have.
Who is DDN Storage India a good fit for in India?
DDN Storage India works best for Indian businesses that already have established workflows around the related platforms, need DPDP-compliant data residency, and want a single accountable partner for deployment plus quarterly tuning. Sirius Star runs the entire lifecycle — scoping, deployment, training, and renewal — from a Navi Mumbai engineering team.
How long does DDN Storage India deployment take?
A typical DDN Storage India rollout in India takes 2-6 weeks from purchase order to production cutover, depending on scope. Sirius Star follows a phased plan: scoping call within 8 working hours of enquiry, design review within 5 days, deployment waves, then a 30-day stabilisation window before handing over to your team or our managed retainer.