Measuring the power of a GPU: NVML, DCGM and the sampling errors that invalidate your wattsAug 29, 2026
The reproducible harness: measuring cost, performance and energy in a single auditable experimentJun 16, 2026
From the SLO to the number of GPUs: how to size and justify the investment in inference hardwareJun 16, 2026
Energy in TCO and in regulation: cost, PUE, CSRD and the new European reporting obligation for datacentersJun 16, 2026
Measurement bias and reproducibility: why two benchmarks of the same model give figures that differ by up to 7×Jun 16, 2026
Full TCO of an on-premise GPU cluster: from capex to the all-in €/GPU-hour and the break-even against cloudJun 16, 2026
Cloud GPU: on-demand, reserved and spot, and why neoclouds change the board (with 2026 prices)Jun 16, 2026
LLM quality benchmarks: the contamination trap and the OSS tools for not fooling yourselfJun 16, 2026
Sovereign on-premise vs hyperscalers: the case with data (cost, energy, performance and sovereignty)Jun 14, 2026
Measuring energy in production: Kepler, DCGM and the practical stack (accuracy and overhead)Jun 14, 2026
Energy per token in Spain: methodology, the electricity market and the cases of speculationJun 14, 2026
Kubecost vs OpenCost vs the alternatives: what the commercial layer adds and when it is worth paying forJun 14, 2026
Cost, performance and energy: the three axes of LLM inference and the identity that ties them togetherJun 13, 2026