10,000 GPUs, One TSDB: Cardinality at GPU Scale
1,000 nodes × 8 GPUs × 60 metrics = 1.4M time series - before you add pod names or Slurm job IDs. GPU monitoring is a cardinality problem disguised as a metrics problem. How to design for it before production OOMs your Prometheus.
Shekhar
From GPU Silicon to Business Metrics: The 8 Layers of GPU Observability
GPU observability isn't one thing - it's eight connected layers from silicon to cost. See why correlation across layers is what cuts debugging from 2 hours to 2 minutes, and why most teams instrument only one or two
Shekhar
The GPU Metrics That Actually Matter
Most teams monitor three GPU metrics - utilization, temperature, memory. There are 50+ that matter, and the ones you skip cause your worst outages. A vendor-neutral guide across NVIDIA, AMD, and Intel Gaudi
Shekhar
Your LLM Is Slower Than You Think
60% GPU utilization and 3-second response times? GPU utilization is the wrong signal for LLM inference. Here's why TTFT, KV-cache pressure, and queue depth - not utilization - predict user-facing latency.
Shekhar
Predicting GPU Failures Before They Cost You
Predict GPU hardware failures 48–72 hours in advance. A guide to the five rate-based signals — ECC error trends, XID events, thermal ramp, row remap exhaustion, PCIe downtraining — and how to combine them into a composite health score.
Shekhar
Every Token Has a Price: Per-Request GPU Cost Attribution
Flat per-token pricing is wrong by 10–50× per request. Prefill vs decode, batch sharing, and cache effects break the math. How to attribute real GPU cost - compute, energy, and dollars - to each inference request.
Shekhar