EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
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Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.
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EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads
EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
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The Energy Cost of Execution-Idle in GPU Clusters
Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.