AI Ceph Throughput Calculator
Sizing storage for AI training clusters is no longer just about capacity—it’s about synchronization bandwidth. If your storage fabric cannot handle the massive “Read Storms” at the start of every epoch or the bursty “Write Storms” during model checkpointing, your expensive GPUs will sit idle.
We built the AI Ceph Throughput Calculator to solve the complexity of distributed storage sizing.
Use this tool to:
- Calculate the exact aggregate Read/Write bandwidth required for your dataset.
- Visualize the impact of Erasure Coding (EC 6+2) vs. Replication on your write penalties.
- Estimate the minimum number of Ceph nodes needed to maintain quorum and performance.
- Generate a PDF report for your architecture review board.
Frequently Asked Questions (FAQ)
A: It depends on your “Epoch Time.” To keep GPUs saturated, you typically need to read your entire training dataset into memory within the first few minutes of an epoch. Use the calculator above to input your Dataset Size and target Epochs Per Hour to see your specific throughput requirement in GB/s.
A: Local NVMe offers the lowest latency for small clusters (typically under 8 nodes). However, once your dataset exceeds the capacity of a single node—or when you need to checkpoint massive models across hundreds of GPUs—distributed storage like Ceph becomes necessary to handle the aggregate throughput and provide resiliency against node failures.
A: Erasure Coding (EC) significantly impacts write performance due to the parity calculation overhead (often a 1.33x to 1.5x penalty compared to raw speed), which can slow down checkpointing. However, EC has a negligible impact on read performance, which is the primary activity during training epochs. Our calculator lets you toggle EC to see the difference.
A: When your required throughput exceeds 50 GB/s or your cluster grows beyond 12 nodes, standard TCP-based storage networking often becomes a bottleneck. NVMe-over-Fabrics (NVMe-oF) reduces the CPU overhead of storage I/O, allowing you to feed data to GPUs faster without stalling the compute nodes.
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