AI infrastructure &
enterprise IT guides

Almost every failure is a retrieval failure. Permissions at retrieval time, chunking that keeps meaning, and the evaluation set nobody builds.

The break-even is a token-volume question. The four cost lines nobody counts, and when residency settles it before cost does.

Why cost rises steeply as either approaches zero, how to tier systems, and the dependency order that ruins untested recovery plans.

Oversized VMs, zombies, forgotten snapshots and stale headroom — and why more vCPU often makes a VM slower.

How cores are counted, what the 16-core minimum does to a small cluster, what the 72-core story really was.

Rack density numbers, the airflow arithmetic, where air cooling stops working — and the four checks that decide whether your delivery can be switched on.

Measured token rates, the 273 GB/s ceiling, batch serving results and two-node pairing — what the spec sheet doesn’t say.

Which NX nodes take GPUs, how MIG and vGPU divide them, what works on AHV — and the limits that break multi-GPU plans.

One die, three cards: the complete spec table NVIDIA never published in one place — and which version fits which rack.

Hardware slices vs time slices, per-card seat counts for RTX PRO and L40S, licences — and where the setup breaks.

96 vs 48 GB, FP4 and MIG that Ada never had — and the cases where the previous generation still does the job.

Identical compute, all the difference in memory: where the gain reaches 3.4× — and where it is exactly zero.

Weights, KV cache, overhead and concurrent users — the working formulas for choosing a GPU for local LLM inference.