Block storage for inference is critical to production AI performance, so your evaluation must go well beyond GPU specifications. Modern inference pipelines often stall on storage rather than compute slowing responses and inflating spend.
Use five practical benchmarks to validate storage choices that capture throughput, IOPS, tail latency, elasticity and workflow integration. Start by measuring how quickly data streams (throughput) and how many small operations complete under concurrency (IOPS).
Since model weights, embeddings and feature stores sit on the hot path, NVMe-backed block storage is usually the default choice for high-speed access during inference. It provides raw block-level volumes, and you typically measure it by throughput, IOPS, latency and provisioning/attach time.
Industry data shows that 42% of companies abandoned most AI initiatives before production, up from 17% last year. This guide presents five benchmarks you should run before selecting any block storage class or vendor.
Why Block Storage Matters for AI Inference?
Block storage is essential for AI inference because it delivers the ultra-low latency and high IOPS needed to feed data to GPUs quickly, preventing bottlenecks and enabling real-time responses. Here are the top benefits:
Minimizing GPU idle time
Modern GPUs process data extremely fast. If storage can’t keep up, GPUs stall on I/O, and expensive compute is wasted. Block storage sustains a rapid, consistent data stream to keep GPUs busy.
Low latency
With direct block-level access, block storage can avoid some of the network and API overhead of remote file or object storage (e.g., HTTP/gRPC calls). You still run a filesystem on top of block, but you can tune it for low latency and predictable I/O patterns. This cuts latency for real-time use cases like fraud detection, autonomous driving and live analytics.
High performance and IOPS
Optimized for transactional workloads, block storage supports many small random reads and writes. That’s critical when large models or vector embeddings that don’t fully fit in memory require fast, selective access to data blocks.
Efficient data handling
Block volumes can be formatted with specific filesystems, for example, ext4 or XFS on Linux, or NTFS on Windows, and tuned per operating system, giving teams granular control to optimize performance for their AI applications. It’s especially effective for structured data and databases common in complex AI pipelines.
Persistence and reliability
Block storage provides durable volumes, so data and model parameters persist across restarts. This is vital for continuous experimentation and stable production deployments.
5 Benchmarks to Consider While Choosing Block Storage
Validate storage decisions by using these five practical benchmarks that capture throughput, IOPS, tail latency, elasticity and workflow integration.
Benchmark 1: Measure Throughput and IOPS
You should start by quantifying how fast the system moves data and handles small operations at your expected concurrency.
What to measure
- Throughput in MB or GiB per second when streaming model weights or large embeddings to saturate GPUs.
- IOPS for many small random reads that power vector search and key value lookups under concurrent load.
How to test
- Use fio inside Kubernetes pods bound to PersistentVolumes to include CSI overheads.
- Model real block sizes, read ratios and queue depths from production traces.
- Ramp single pod, then multi pod tests to expose contention and noisy neighbor effects.
Targets and signals
- Local PCIe 4.0/5.0 NVMe SSDs can reach millions of IOPS; networked block volumes deliver lower, more predictable IOPS depending on the service tier.
- Treat vendor ratios like 16 IOPS per MiB per second as scaling guides, then verify with your workload.
- Normalize throughput per dollar and IOPS per dollar at your latency targets.
Benchmark 2: Measure Latency Under Real Inference Load
Then, validate p95 and p99 latency under concurrent traffic with sudden spikes because tail behavior dictates user experience and GPU efficiency.
What to measure
- Storage latency distributions alongside end-to-end response time SLOs at target queries per second.
- GPU telemetry for I/O wait versus compute to quantify idle accelerator time caused by storage stalls.
How to test
- Replay realistic patterns such as interactive chat, fraud scoring and clinical inference with bursts.
- Include cold starts by provisioning volumes and timing first model loads after pod scheduling.
- Exercise NVMe over Fabrics or RDMA paths where available to reduce network hops and CPU overhead.
Targets and signals
- Use published results like 351 GiB per second sequential reads with GPUDirect Storage as directional ceilings.
- Expect latency reductions to lift effective GPU utilization by 2 to 3 times in I/O bound stacks.
Benchmark 3: Validate Scalability and Elasticity
Next, prove the platform scales performance and capacity independently while keeping latency stable during peaks and changes.
What to measure
- Time to raise or lower provisioned throughput and IOPS without disrupting running inference services.
- Read concurrency scaling by attaching a high-performance, read-only volume to dozens or thousands of model servers.
How to test
- Trigger rapid traffic spikes from campaign launches, A/B tests and seasonal surges with autoscaling enabled.
- Measure aggregate throughput and p95 or p99 latency across tenants as reader counts increase.
- Expand capacity only, then performance only, confirming automatic rebalancing without manual sharding.
Reliability under load
- Induce node failures, rolling upgrades and zone impairments during peak load and record failover times.
- Require no dropped requests beyond error budgets and no longer failover events that violate SLOs.
- Targets and signals.
- Treat hyperscaler claims of near 1.2 TiB per second aggregate reads as references, then validate locally.
- Prefer designs that add NVMe drives or nodes and immediately redistribute load with minimal operator effort.
Benchmark 4: Quantify Cost Efficiency at Scale
After performance, translate results into business metrics that reflect delivered throughput at your latency goals.
What to measure
- Cost per 1,000 inferences at target p95, including provisioned performance, capacity, snapshots and retention.
- Egress and request charges for hybrid or multi-region paths that serve edge or regulated workloads.
How to test
- Rerun Benchmarks 1 and 2 at steady state QPS and during bursts while metering spend.
- Model on-prem, cloud and hybrid placements using identical scripts to compare apples to apples.
- Convert storage improvements into GPU count reductions through utilization gains and faster model loads.
Targets and signals
- Expect 3 to 5 times faster model loads to cut warmup windows and reduce idle time materially.
- Expect 2 to 3 times higher GPU utilization to shrink the accelerator fleet required for a given QPS.
Benchmark 5: Verify Framework and Workflow Integration
Finally, ensure storage behaves correctly with your serving stack and operational workflows under continuous change.
What to measure
- Provisioning time to create, attach, resize and detach volumes through standard CSI drivers in Kubernetes.
- Correct behavior with TensorFlow Serving, PyTorch, NVIDIA Triton, Ray Serve and custom gRPC services.
How to test
- Execute blue green rollouts using snapshots and clones, then time model promotion and rollback steps.
- Validate NVMe support, NVMe over Fabrics paths and GPUDirect Storage for CPU bypass and reduced latency.
- Exercise RAG and key value caching with many small objects, read and write to confirm metadata performance.
Targets and signals
- Prefer configurations that meet to attach and resize SLOs within seconds during rolling deployments.
- Confirm that file or object tiers handle datasets and checkpoints, while block storage serves on the hot path.
Turn Benchmarks into Live Inference Wins with AceCloud
Treat storage as the throttle for inference performance, then validate it with benchmarks that tie directly to GPU utilization. With AceCloud, you can launch NVMe-backed block volumes alongside H200, A100, L40S, RTX Pro 6000 or RTX A6000 instances and measure hot-path throughput.
Capture p95 and p99 latency, correlate I/O wait with GPU telemetry, then quantify cost per 1,000 inferences at SLO. Scale capacity and performance independently, verify read concurrency scaling across nodes and confirm attach, resize and snapshot times during rolling upgrades.
These results right-size your GPU fleet, reduce tail latency, improve reliability under faults and deliver consistent responses during traffic spikes. Start AceCloud’s no-cost benchmarking engagement, migrate with expert assistance, and turn storage from a bottleneck into a measurable competitive advantage today.
Frequently Asked Questions:
You’ll typically combine local NVMe (ephemeral) or high-performance network block with object storage and possibly a file layer, depending on how your models are loaded and shared.
High storage latency increases p95 and p99 response times and reduces GPU utilization, which raises the number of GPUs needed to meet SLOs.
For latency-sensitive inference hot paths, NVMe-backed block storage is usually the best choice because it delivers lower latency and better random I/O. File or object storage remain ideal for datasets, checkpoints, model registries and archives, especially when models are pre-loaded into RAM or GPU memory.
Focus on throughput and IOPS, latency under real load, scalability and elasticity, cost per 1,000 inferences, plus compatibility with your frameworks and platform.
