Scaling ML Models Efficiently With Shared Neural Networks
SMRTR summary
A new decoupled architecture for machine learning models combines shared neural encoders with specialized prediction heads, addressing memory constraints and scaling challenges. This approach reduces model memory usage from 210 MB to 68 MB, improves latency by 40%, and allows a single server to handle 1,500 transactions per second with 1,000 active models.
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