Benchmark: Filesystem and Object Layer Choices for High‑Throughput ML Training in 2026

Hook: Training Jobs Don’t Care About Theory — They Care About Sustained Throughput

ML clusters in 2026 demand both throughput and predictable behavior under hundreds of concurrent workers. This benchmark compares Lustre-style parallel filesystems, optimized object layers over NVMe-oF, and local ZNS-backed filesystems to determine best-fit patterns for common training workloads.

Benchmark goals and methodology

We measured sustained read throughput, checkpoint restore time, metadata scalability, and tail latency under 128 concurrent workers using real datasets (100TB to 1PB scale). Tests were repeated across filesystem choices and namespace placements.

Key results

• Parallel filesystems delivered consistent throughput but required heavy metadata coordination.
• Object layers over NVMe-oF matched throughput with simpler horizontal scaling but needed careful placement to avoid hotspots.
• ZNS-backed local pools reduced write amplification and improved checkpointing speeds when writes were zone-aligned.

Tuning recommendations

1. Use large multipart objects or aggregated files to reduce metadata pressure.
2. Align checkpoint writes to zone boundaries for ZNS media to reduce GC.
3. Apply QoS at the fabric level rather than device-level where possible to prevent noisy neighbors.

Operational insights

Observed operational challenges:

• Metadata contention is often the limiting factor, not raw device bandwidth.
• Telemetry and canary practices across storage and scheduler changes reduce incidents; see Zero-Downtime Telemetry.
• Creators building short-form models and datasets need predictable egress patterns; understanding content discovery pipelines from short-form algorithm evolution helps predict bursts — see The Evolution of Short‑Form Algorithms.

When to choose each approach

• Massive parallel training: parallel filesystems for tightly-coupled workloads with strong metadata services.
• Elastic training clusters: object layers over NVMe-oF for easier recruitment and release of resources.
• Hybrid labs: ZNS pools for checkpointing, with object layers for dataset distribution.

Integrations worth exploring

Consider pairing storage strategies with developer tooling and document workflows. For instance, small firms comparing cloud document workflows vs local OCR pipelines should see the practical verdict in DocScan Cloud OCR vs Local Document Workflows to understand when local processing is beneficial.

Future predictions

Filesystem semantics will increasingly be expressed as policies in orchestration layers, enabling dynamic adaptations to workload signals and facilitating the growth of composable storage fabrics.

Conclusion

There is no one-size-fits-all filesystem for ML training in 2026. Choose based on metadata pressure, restore SLAs, and operational maturity. Instrument heavily and use canary updates for all storage and scheduler changes.

Author: Diego Marquez — Performance Engineer. I run large-scale ML cluster benchmarks and advise on storage-scheduler co-design.