Kubernetes storage for AI workloads encompasses the persistent volume infrastructure that backs GPU-accelerated training jobs, model checkpoints, inference caches, and vector databases running on Kubernetes. Unlike general-purpose stateful workloads, AI and machine learning pipelines impose diverse and often contradictory storage requirements: sequential read throughput for dataset ingestion, burst write bandwidth for checkpointing, and low-latency random IOPS for vector database queries — often simultaneously on the same cluster.
The central challenge is GPU utilization: a GPU cluster sits idle when storage cannot deliver data fast enough to keep compute busy. Storage is the most common non-compute bottleneck in AI infrastructure, and the problem is compounded on Kubernetes because multiple workloads — training jobs, serving pipelines, data preprocessing — compete for the same storage fabric simultaneously.
AI/ML Storage I/O Patterns on Kubernetes
AI and ML workloads segment into four distinct storage use cases, each with its own I/O profile:
Dataset ingestion (training data reads): large sequential reads of multi-GB training files. The bottleneck is aggregate read throughput from storage to GPU nodes. Object storage (MinIO, S3) handles raw datasets; block storage is appropriate when preprocessing steps need random access to dataset shards.
Checkpoint storage (burst writes during training): at regular intervals, the training framework (PyTorch, TensorFlow, JAX) writes the full model state to persistent storage. Large language models can produce checkpoints of 100 GB to several TB. These writes are bursty and must complete quickly — if checkpoint writes stall, training pauses or the GPU sits idle waiting for the write to complete.
Model serving and inference caches: inference serving reads model weights at startup and keeps them in memory, but frameworks that use paged attention or KV caches also write and read from NVMe-backed volumes during serving. The pattern is medium-size random reads at consistent IOPS.
Vector databases: systems like Weaviate and Pinecone that power retrieval-augmented generation (RAG) pipelines use storage intensively for index segments. The pattern is small random reads and writes with high IOPS requirements per query.
Object Storage vs. Block Storage for AI
AI platforms commonly use both storage types at different stages of the pipeline:
| Use case | Storage type | Why |
|---|---|---|
| Raw training dataset storage | Object storage (MinIO/S3) | Cost-efficient for large unstructured files; good streaming throughput |
| Training checkpoints | Block storage (NVMe PVC) | Burst write bandwidth; random read for resume; low-latency for frequent saves |
| Vector database indexes | Block storage (NVMe PVC) | High random IOPS; consistent query latency |
| Inference model cache | Block storage (NVMe PVC) | Fast model load at startup; page cache efficiency with NVMe |
| Feature stores | Block or object | Depends on access pattern; block preferred for high-frequency feature retrieval |
MinIO is the most common self-hosted object storage for AI dataset pipelines. Block storage handles the latency-sensitive parts of the stack.
QoS Isolation Between Training and Inference
Multi-tenant AI platforms on Kubernetes face a specific conflict: training jobs run large checkpoint writes that can saturate the storage fabric, causing latency spikes in inference serving pods on the same cluster. Without storage-level QoS isolation, a single large training run can cause inference timeout errors for production serving endpoints.
Storage QoS enforcement — per-volume IOPS and bandwidth limits at the storage layer — allows platform teams to separate training and inference into distinct performance tiers. Training checkpoints receive high burst throughput but are capped so they cannot consume the entire fabric. Inference volumes receive a guaranteed IOPS floor that is protected from training load spikes.
🚀 GPU-ready block storage for AI workloads on Kubernetes Simplyblock provides NVMe/TCP and NVMe/RoCE block volumes with multi-tenant QoS that separates training checkpoints from inference latency SLOs. 👉 Kubernetes storage for stateful workloads
Sizing Storage for AI Workloads
Checkpoint volume sizing depends on model size, checkpoint frequency, and retention. A practical formula:
Checkpoint PVC size ≈ model_size_GB × checkpoints_retained × safety_factor (1.5x)
For a 70B parameter model (roughly 140 GB in BF16), retaining 3 checkpoints requires approximately 630 GB with safety margin. Thin provisioning allows platforms to allocate this space without pre-consuming it — the PVC grows as checkpoints are written.
Vector database volumes are sized by index footprint. Weaviate and similar systems report estimated index sizes during data ingestion; a common starting point is 1.5–2x the raw embedding data size. Plan for growth as the corpus expands.
Kubernetes Storage for AI with Simplyblock
Simplyblock provides CSI-provisioned block volumes backed by NVMe/TCP or NVMe/RoCE, suited to the demanding I/O profile of AI/ML infrastructure. Specific capabilities relevant to AI workloads:
- High sequential throughput for checkpoints: NVMe/TCP or NVMe/RoCE delivers the burst write bandwidth that training frameworks need for fast checkpoint saves without GPU stalls.
- Multi-tenant QoS: separate IOPS and throughput ceilings for training and inference tiers. Training jobs cannot saturate the fabric and spike inference serving latency.
- Thin provisioning for checkpoint volumes: allocate generously sized checkpoint PVCs without consuming physical capacity upfront. Volumes grow on demand as checkpoints accumulate.
- Storage performance isolation: each namespace or workload tier gets its own enforced performance envelope, which is essential for shared GPU clusters where multiple teams share infrastructure.
- Instant snapshots: create consistent snapshots of vector database index volumes for backup or cross-environment cloning without interrupting serving traffic.
Related Terms
These glossary entries cover the storage backends and adjacent concepts for AI workloads on Kubernetes.
- What Is Weaviate
- What Is Pinecone
- What Is MinIO
- Storage Performance Isolation
- Stateful Workloads on Kubernetes
Questions and Answers
What storage do AI training workloads need in Kubernetes?
AI training workloads require block storage that delivers high burst write throughput for checkpoint saves and sufficient sequential read bandwidth to keep the data loading pipeline ahead of GPU compute. Each training pod should have a dedicated PVC mounted as a block device; NVMe-backed volumes on NVMe/TCP or NVMe/RoCE deliver the throughput needed to avoid GPU stalls during checkpointing. Training jobs should be on a separate storage QoS tier from inference serving to prevent checkpoint writes from causing latency spikes elsewhere.
How do I store ML model checkpoints in Kubernetes?
Create a PVC with a StorageClass backed by high-throughput NVMe block storage and mount it in the training pod at the checkpoint directory. Size the PVC using the formula: model size in GB multiplied by the number of checkpoints to retain, plus a 1.5x safety margin. Use thin provisioning if the storage backend supports it so the PVC can be allocated generously without pre-consuming physical capacity. Consider using CSI VolumeSnapshots to create consistent point-in-time copies of the checkpoint volume for backup or resume-from-snapshot workflows.
What is the best block storage for GPU clusters?
NVMe/TCP is the most practical choice for GPU cluster storage: it runs over standard Ethernet switching, delivers sub-millisecond latency and multi-GB/s throughput per volume, and requires no specialized fabric hardware. NVMe/RoCE is the alternative for environments with RDMA-capable networking where the absolute lowest latency is required. Both transports are supported by simplyblock. The key additional requirements are multi-tenant QoS (to isolate training from inference tiers) and thin provisioning (for flexible checkpoint volume sizing).
How do I prevent storage from bottlenecking GPU utilization?
The primary prevention measures are: provision enough aggregate storage bandwidth for the expected checkpoint write rate, enforce QoS limits so no single training job can saturate the fabric, use fast NVMe block storage rather than object storage or NFS for checkpoint and vector database volumes, and monitor storage latency percentiles (p95/p99) alongside GPU utilization metrics. When checkpoint writes stall, GPU utilization drops to near zero during the write window. Setting storage throughput guarantees per training job, matched to the expected checkpoint frequency and model size, keeps GPU utilization high.