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GPU Shortages Remain a Major Bottleneck for ML Teams Despite Growing Supply

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Despite widespread headlines about GPU proliferation, machine learning teams in 2025-2026 continue to face frequent GPU unavailability when launching training jobs on cloud platforms. Key causes include regional fragmentation of GPU inventory, inflexibility from reserved-capacity commitments, and the inherently spiky nature of ML workloads that mismatches steady-state cloud capacity planning. Experts suggest decoupling job submission from execution using queue systems, tracking instance availability alongside cost, and right-sizing GPU requests rather than defaulting to the largest SKUs. Separating interruptible workloads to use spot or preemptible instances — which have deeper availability pools — is also recommended as a high-leverage engineering investment. While no single fix resolves the structural supply-demand gap, teams that proactively build fallback strategies tend to experience far less disruption than those improvising solutions mid-incident.

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