Block Storage for AI and GPU Workloads: Why Cloud SAN Is the Right Architecture

GPU utilization is the metric AI teams track most closely — and the one most quietly dragged down by a storage layer nobody stress-tested before the workload went live. A training job starts, GPU utilization sits at 40%, and days get spent tuning batch sizes and prefetch buffers before the real culprit surfaces: block storage that cannot keep pace with what the accelerators can consume. Getting your GPU infrastructure for AI and ML right means the storage backend has to match it in performance — or the cluster is just waiting.

This is an architectural problem, not a configuration one. Block storage built for web servers and relational databases behaves differently under the I/O patterns of AI training, inference serving, and large-scale preprocessing pipelines. Getting it wrong doesn’t generate errors — it generates an expensive cluster running below its actual ceiling.

What AI Workloads Actually Demand From Block Storage

The storage requirements for AI and ML workloads split across three distinct dimensions, and each one matters for a different part of the pipeline.

Training needs throughput above everything else. A large model training job reads batches of samples repeatedly across many epochs, often from millions of files. At cluster scale, aggregate read throughput requirements can reach 10 to 100+ GB/s. A single NVIDIA H100 GPU at full capacity processes data faster than many standalone block volumes can physically deliver it. When storage falls behind, GPUs idle — and idle GPU time in cloud infrastructure still runs on the meter.

Preprocessing demands deep I/O queues. Tokenization, normalization, and shuffling pipelines hit storage with high-volume random I/O. NVMe supports up to 65,535 parallel queues at 65,535 commands each. Legacy protocols like iSCSI cap out at a single queue of 32 commands. For preprocessing workloads hitting storage concurrently, that gap translates directly into pipeline stalls.

Inference requires low latency in the request path. When a deployed model handles a live request and needs to retrieve from a key-value cache or load embedding vectors, that storage read sits inside the critical path of the API response. Storage latency at this stage shows up directly in your p99 response time distribution — there is no software layer that absorbs it.

Where General-Purpose Cloud Block Storage Falls Short

AWS EBS, Azure Managed Disks, and Google Persistent Disk are well-engineered for the workloads they were designed around: web applications, relational databases, and virtual machines with predictable, moderate I/O. Multi-GPU training clusters are a different class of problem.

Per-volume throughput caps create hard ceilings. Most public cloud block volumes top out between 500 MB/s and 4 GB/s per volume. A 16-GPU cluster can consume multiples of that. Teams stripe across many volumes as a workaround — which adds operational complexity, multiplies failure surface, and still frequently cannot reach the aggregate bandwidth a Cloud SAN delivers natively from a single storage pool.

Shared tenancy introduces latency noise. Hyperscaler block storage runs on shared infrastructure across thousands of tenants. Latency spikes during peak demand windows — not consistently, but unpredictably. That variance shows up as jitter in training step times and instability in inference response distributions. It is difficult to diagnose and impossible to eliminate through configuration.

Egress fees scale with AI data volumes. Moving training datasets between regions, loading model weights, or exporting results triggers data transfer charges. At the volumes AI pipelines generate — often hundreds of terabytes monthly — those fees become a budget line that wasn’t visible during initial planning.

What a Cloud SAN Architecture Changes

A Cloud SAN places block storage on a private, dedicated fabric — separated from shared public infrastructure and co-located with compute on a high-bandwidth internal network. For AI workloads, three things change in ways that matter.

Dedicated bandwidth holds under concurrent load. The throughput a GPU cluster is allocated in a Cloud SAN is the throughput it receives, including during peak hours and when every node in a training cluster is reading simultaneously. This is not a ceiling that degrades when neighboring tenants saturate the shared fabric — it is reserved capacity.

NVMe-oF keeps latency where inference needs it. A properly built storage architecture for AI runs NVMe SSDs on the backend presented to compute over NVMe over Fabrics (NVMe-oF). End-to-end latency stays in the sub-100-microsecond range — a meaningful gap from the 1–5ms typical of iSCSI-backed public cloud block volumes. For the protocol mechanics behind this, the earlier post on NVMe over Fabrics for enterprise Cloud SAN covers the transport layer in depth.

Parallel access scales cleanly. When 32 or 64 GPU nodes read training data simultaneously, the storage fabric needs to handle that fan-out without degrading. A Cloud SAN with a well-designed network fabric routes concurrent I/O across dedicated bandwidth paths rather than serializing requests through shared infrastructure that wasn’t built for this access pattern.

Training vs. Inference: Two Different Storage Problems

Treating training and inference as the same storage problem leads to architecture decisions that solve one and fail the other.

Training is a bulk sequential read problem. The job reads the same dataset repeatedly, across millions of files, while keeping a GPU cluster continuously fed. Aggregate throughput and parallel multi-node access are what matter. A Cloud SAN with shared NVMe block storage and sufficient fabric bandwidth handles this well.

Inference is a latency problem. Storage reads in the critical path of a live request — fetching cached embeddings, loading model shards — add directly to response time. The compute platform and storage layer need to be co-designed for this. A fast GPU behind slow storage has a performance ceiling that no application-level optimization can fully overcome.

What to Measure When Evaluating AI Block Storage

p99 latency at queue depth 128. Average latency under light load is easy to engineer for. Tail latency under the deep, concurrent queues GPU clusters generate is where overcontended storage reveals its actual floor. Ask for this number specifically.

Transport protocol: NVMe-oF or iSCSI? Some providers advertise “NVMe storage” while delivering it over iSCSI at the network layer. The drives are fast; the protocol reintroduces overhead. Ask explicitly whether the storage fabric runs NVMe-oF end-to-end from drive to compute host.

Aggregate cluster throughput, not per-volume specs. For AI deployments, the binding constraint is aggregate bandwidth when all GPU nodes read concurrently — not any single volume’s spec sheet ceiling.

Egress pricing at volume. Review the pricing structure before the first large training run, not after the first invoice. At AI data volumes, transfer costs become a real variable.

IOPS and throughput SLAs, not just uptime. Availability commitments are baseline. SLAs that commit to IOPS delivery and throughput floors under concurrent load are what matter for production AI infrastructure.

Where Hybrid Cloud Fits

Many organizations run preprocessing or annotation workflows on existing on-premises infrastructure before moving data to GPU clusters, or deploy trained models to public-facing endpoints while keeping training environments private. A hybrid cloud architecture lets block storage span private Cloud SAN and external infrastructure without crossing a public internet boundary on every data access — a practical option for teams that need environment flexibility without sacrificing storage performance.

Frequently Asked Questions

What block storage protocol is best for AI training?

NVMe over Fabrics (NVMe-oF) is the right protocol for AI training at scale. It delivers sub-100-microsecond latency and supports deep parallel I/O queues that match the concurrent read patterns GPU clusters generate. iSCSI, with a single 32-command queue and 1–5ms latency, creates a bottleneck that becomes visible as GPU utilization drops during training.

Why do GPU clusters underperform when block storage is slow?

GPUs can only train as fast as data arrives. When block storage throughput falls below what the cluster can consume, accelerators sit idle waiting for the next batch — a condition called I/O-bound training. It extends run duration, inflates compute cost, and produces no improvement in model output. The fix is storage architecture, not GPU configuration.

Can general-purpose cloud block storage handle production AI workloads?

At small scale, yes. At production scale — multi-GPU clusters, high-throughput inference, or pipelines moving hundreds of terabytes — the per-volume throughput caps, shared tenancy noise, and egress pricing create constraints that workarounds only partially address. Most production AI infrastructure outgrows general-purpose cloud block storage as workload scale increases.

Conclusion

Block storage is not a commodity decision when GPU clusters are involved. The throughput requirements of training, the latency requirements of inference, and the concurrent access patterns of multi-node clusters all require a storage architecture that general-purpose cloud block volumes were not built to handle at production scale.

Cloud SAN — with dedicated bandwidth, NVMe-oF transport, and a fabric purpose-built for the workloads running on top of it — closes the gap between what a GPU cluster can process and what storage can actually deliver. For teams that need managed services alongside the infrastructure, MarQi Cloud is built specifically for this class of workload.