Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Overall Results | HackerNoon
Briefly

from Hackernoon4 months ago
Apparate significantly lowers latencies compared to vanilla model serving, achieving median speedups that range from 40.5-91.5% across CV workloads, maintaining a strict 1% accuracy constraint.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-overall-results
The efficiency of Apparate improves with larger model sizes, yielding notable latency savings, especially for extensive models like GPT-2 and BERT-large, emphasizing its tailored architecture.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-overall-results
Read at Hackernoon
#model-serving #latency-optimization #early-exits #machine-learning #performance-evaluation
[
|
]