The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
Available from: https://arxiv.org/ abs/2412.17376
2 Pith papers cite this work. Polarity classification is still indexing.
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MPS can boost performance up to 30% and cut energy 20% with careful provisioning but degrades sharply under memory contention, whereas MIG delivers steadier gains through hardware isolation at the cost of higher overhead and occasional performance losses.
citing papers explorer
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Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
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A comprehensive evaluation of spatial co-execution on GPUs using MPS and MIG technologies
MPS can boost performance up to 30% and cut energy 20% with careful provisioning but degrades sharply under memory contention, whereas MIG delivers steadier gains through hardware isolation at the cost of higher overhead and occasional performance losses.