In distributed AI workloads, training pipelines must balance a wide range of I/O behaviors: streaming high-throughput training data, fetching validation sets, writing logs, and, critically, performing periodic model checkpointing. While all these tasks are essential, not all I/O is equally time-sensitive. Model checkpointing, in particular, can introduce large, bursty write loads that compete with latency-sensitive reads from the training dataset.
Unthrottled, this competition can degrade the end-to-end training performance, underutilize GPUs, and introduce variability in job runtimes.
To address this, VAST Data offers dynamic quality of service (QoS), a policy-driven mechanism that allows administrators to throttle and prioritize I/O at the tenant, directory, bucket, and user levels, ensuring that critical training operations are not starved by auxiliary write-heavy workloads like checkpointing.
The Problem: Checkpoints as a Noisy Neighbor
Model checkpoints are often MB to GB-scale writes triggered at fixed intervals. Because they’re typically flushed to disk in large, sequential writes (sometimes across hundreds of training workers simultaneously), they can saturate the storage system’s IOPS and bandwidth, especially if they share the same namespace and underlying infrastructure as data loaders and other training-time dependencies.
This results in a classic resource contention problem: time-sensitive data reads slow down due to I/O backpressure from low-priority writes.
The Solution: Fine-Grained, Dynamic QoS
VAST systems allow administrators to assign QoS limits per tenant, per directory, per S3 bucket, or per user principal. This enables precise control over how I/O bandwidth and IOPS are allocated across competing workflows.
For example, a common pattern in training environments might look like this:
With VAST’s QoS you can:
Cap throughput on /mnt/mlfs/checkpoints/ to a fixed MB/s or IOPS threshold, preventing bursty checkpoint dumps from interfering with concurrent reads from /mnt/mlfs/data/.
Alternatively, enforce bucket-level QoS if workloads interact with VAST via S3, allowing object-level ingest (e.g., s3://checkpoints-bucket/) to be rate-limited independently of dataset buckets.
Apply QoS for multi-user clusters at the user level, ensuring fair-share enforcement and preventing a single user from monopolizing storage I/O.
These policies are enforced dynamically and in real time, meaning the system can adapt to changing workload conditions without requiring manual reconfiguration or job restarts.
Technical Benefits
1. Predictable GPU Utilization By ensuring that training data reads are not blocked by checkpoint writes, GPU starvation is minimized and training throughput becomes more consistent.
2. Pipeline Stability Across Jobs In shared environments where multiple training jobs run in parallel, isolating I/O by path or user guarantees that one job’s checkpoint routine doesn’t degrade the performance of others.
3. Easy Integration with Existing Training Workflows No application-level changes are required. QoS enforcement occurs at the file system or object store layer, and integrates seamlessly with POSIX and S3 access patterns.
4. Simplified Multi-Tenant Management With user-based QoS, administrators can provide tenant-level guarantees and prevent overuse without needing to containerize or isolate jobs physically.
5. Dynamic QoS Allocation QoS settings can be configured and updated on the fly with changes taking effect within a second.
Throttle Checkpoints, Protect Performance
Checkpointing is essential, but when left unchecked it can jeopardize the performance of the entire training pipeline. VAST QoS provides a robust mechanism for I/O shaping, allowing checkpoint ingest to be throttled without impacting overall training progress.
Want help integrating VAST QoS into your AI stack? Let’s dive into how to scope throughput guarantees based on your model size, checkpoint frequency, and job concurrency.
How do you currently handle I/O contention in your AI training environments? If you’ve implemented QoS policies before—whether with VAST or another platform—what lessons did you learn? Share your experiences, questions, or best practices on Cosmos, where AI practitioners learn from each other and make AI infrastructure better for everyone.
