Inference is latency-bound and concurrent — you're serving many simultaneous requests and time-to-first-token (TTFT) is the critical SLA. The biggest lever is KV Cache offload: as part of the Dell AI Data Platform, NVIDIA CMX enables KV cache offload to shared Dell AI Storage — delivering up to 19× faster TTFT vs. standard vLLM and letting you serve more concurrent sessions on the same GPU hardware. S3 over RDMA adds 230% higher throughput and 80% lower latency on object-heavy inference pipelines.

Dell eliminates all three simultaneously. GPUDirect Storage bypasses the CPU entirely — data moves directly from Dell AI Storage to GPU VRAM, eliminating bounce buffers and doubling peak bandwidth. NFS over RDMA removes OS networking stack overhead entirely. And SmartPools auto-tiering ensures hot training data is always on NVMe flash — no manual policy, no ops tickets, no stalls.

1. Are you NVIDIA-certified — and at what scale? NCP certification at 16k+ GPU configurations is the bar. Ask for the reference architecture document, not a slide.
2. Does your storage support GPUDirect and NFS over RDMA natively? These eliminate CPU bottlenecks. If they require third-party add-ons or workarounds, that's a red flag.
3. How do you handle multi-tenant isolation? In production AI environments — whether CSP or enterprise — hard namespace isolation (not best-effort) is required. Ask about ZRBAC and Access Zones.
4. What does your power and rack footprint look like at scale? At 16k+ GPU NCP scale, Dell PowerScale uses 72% less power and 88% fewer switches than alternatives. Ask for the validated comparison.
5. What does Day 2 look like? Ask about monitoring, non-disruptive upgrades, Kubernetes CSI driver support, and what ProSupport or managed services are available.

• 100–500 GPUs (enterprise entry): PowerScale all-flash with GPUDirect + NFSoRDMA. Focus on checkpoint throughput and dataset capacity. APEX pay-per-use keeps capex low while you validate workloads.
• 500–4,000 GPUs (mid-scale): PowerScale + ObjectScale in a tiered architecture — NVMe flash for hot training data, S3 object for datasets and model artifacts. OneFS global namespace eliminates silos.
• 4,000 - 16k+ GPUs *(hyperscale/neocloud): Dell PowerScale and ObjectScale are perfectly suited to handle up to 16k+ GPUS. We also offer Dell Exascale Storage, our software-defined 4-in-1 portfolio, for extreme-scale environments.

Dell's architecture team provides free sizing engagements — including TCO modeling — as part of the briefing process.

1. Assess & size: Dell Professional Services inventories your existing data landscape, identifies AI-ready workloads, and maps them to the right storage tier — before you buy anything.
2. Design & validate: A reference architecture is built and validated against your specific GPU environment, data volumes, and latency requirements.
3. Build & integrate: PowerScale's OneFS global namespace means you can onboard new data sources — NFS, SMB, S3, HDFS — without rewriting applications. Existing workflows keep running.
4. Deliver & deploy: Dell deploys, configures, and hands off a production-ready environment — with ProSupport for ongoing monitoring and non-disruptive upgrades.

APEX flexible consumption means you can start with exactly the capacity you need today and scale non-disruptively as data grows — no forklift upgrades.

• At rest: SED DARE encryption + WORM-locked immutable snapshots protect training datasets and model checkpoints from ransomware and tampering.
• In flight: NFS-over-TLS encrypts all data in transit across the storage fabric — critical for financial services, healthcare, and federal workloads.
• Access control: ZRBAC enforces per-tenant, per-workload, per-agent policies — no manual approval chain needed for automated AI pipelines.
• Compliance: FIPS 140-3, DISA STIG, DoD APL, SEC 17a-4, HIPAA, and FedRAMP Ready — with full audit trails and Data Masking for sensitive fields.

Threat detection: AI-powered anomaly detection spots unusual access patterns before they reach production pipelines. SmartQuotas contain blast radius at the namespace level.

1. Missing or wrong NVIDIA certification. Teams buy storage that claims "AI-ready" but isn't NCP-validated at the GPU count they're deploying. The result is throttled throughput, failed DGX SuperPOD deployments, and months of remediation. Always ask for the validated reference architecture at your specific scale.
2. Undersized namespaces and manual tiering. Static storage configurations can't keep up with AI data growth. When hot training datasets spill to cold tiers manually, GPU idle time spikes. Dell SmartPools auto-tiering and OneFS global namespace eliminate both problems — data is always where it needs to be, automatically.
3. Protocol lock-in creating data silos. Teams build separate NFS clusters for training, S3 buckets for datasets, and block storage for databases — then spend months trying to connect them. Dell's unified file + object platform (NFS, SMB, S3, HDFS on one namespace) eliminates silos before they form.
4. No Day 2 plan. Deploying AI storage is step one — operating it at scale is harder. Teams that skip monitoring, non-disruptive upgrade planning, and Kubernetes CSI integration end up with fragile infrastructure. Dell ProSupport, APEX managed services, and Dell CSM for Kubernetes provide the operational layer most teams underestimate.

Dell supports the technologies customers run today while helping them adapt as their environments and priorities evolve. Standardize where it makes sense. Specialize where it matters. Bring new technologies and innovations into your strategy at the pace that works for your business, without forcing a reset of what already works. To learn more, please read our Gartner Magic Quadrant blog: https://www.dell.com/en-us/blog/dell-named-a-leader-in-the-2026-gartner-magic-quadrant-x2122-for-enterprise-storage-platforms/