AI training checkpoint writes to cloud object storage incur 30–40 ms of latency per operation and trigger S3 throttling at scale. This guide explains the root causes, surveys every major architectural approach, and describes the write-back caching architecture that eliminates the problem. 

The write-back caching architecture is particularly relevant for teams operating across multiple clouds, GPU providers, or on-premises environments that need a portable way to accelerate checkpoint writes while preserving object storage as the system of record.

Who this guide is for: MLOps engineers, AI infrastructure leads, and platform teams responsible for distributed training at scale — whether operating a shared training platform across hundreds of concurrent jobs or building infrastructure for a single large training run.

What this guide covers: The root causes of checkpoint-induced GPU idle time; a comprehensive survey of checkpoint architectures with scenario-based guidance on where each fits; and a technical description of the write-back caching architecture — including how Alluxio Write Cache implements it in production across multi-cloud and hybrid environments.

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We checkpoint and restart as often as we can.
- Jensen Huang  CEO and Co-Founder, NVIDIA