GPU platforms that pay back
Capacity, scheduling and cost controls for shared GPU estates running mixed training and inference workloads across teams.
GPU spend is the new cloud-bill shock. The platforms that pay back share three properties: they're shared, scheduled, and accountable.
Shared, not assigned
Per-team GPU pools sit idle 70% of the time. A single shared pool with quotas, priorities and pre-emption gets utilisation above 80% without anyone feeling starved.
Schedule for the workload mix
Inference wants low-latency, bin-packed allocation. Training wants large, contiguous reservations. A single scheduler tuned for both — Karpenter plus Volcano, or equivalent — beats two separate clusters every time.
Show teams the bill
Per-namespace cost dashboards backed by accurate GPU-hour metering change behaviour within a week. Suddenly the 'always-on' notebook gets a shutdown timer.
More insights
Landing zones that survive an audit
A pragmatic walkthrough of multi-account AWS landing zones built for SOC 2 and ISO 27001 — what to centralise, what to delegate, and where automation pays back fastest.
Read ReliabilitySLOs without the theatre
How to define error budgets that engineers actually use, and how to wire them into deployment decisions instead of quarterly slide decks.
Read SecurityZero-trust network design for hybrid estates
Identity-aware proxies, private service connect and short-lived credentials — a practical pattern set for organisations migrating off perimeter security.
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