Company updates and observations on the compute and AI infrastructure market.
Hardware selection, cooling technology, workload scheduling and energy sourcing are the four practical levers for reducing the energy intensity of GPU fleets.
Training and inference workloads behave very differently, and contracting models are adapting to serve both without forcing them into one shape.
Renting rather than buying extends device lifecycles, reduces electronic waste and keeps capital available for core operations.
A practical overview of how token-based AI services are metered, how quotas are managed and how enterprises keep inference costs predictable.
Where liquid cooling pays off for dense GPU deployments, and where the practical trade-offs sit beyond the headline efficiency figure.
Three sourcing models suit three different workload profiles. The deciding factors are duration, utilisation variance and how much operational burden a team wants to carry.
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nimblegpu
nimblegpu
