10,000 GPUs, One TSDB: Cardinality at GPU Scale

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GPU monitoring at scale creates a cardinality explosion in time-series databases. A 1,000-node cluster with 8 GPUs each and 60 metrics per GPU generates 1.4M unique time series from hardware metrics alone — before adding pod names, Slurm job IDs, or model versions. The core architectural solution is splitting high-cardinality

8m read timeFrom last9.io
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Table of contents
Why GPU Monitoring Explodes CardinalityThe Logs vs Metrics SplitDimension PruningInterval Processing and AggregationWhat Breaks at ScaleDesign for Scale on Day One

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