You are deploying a language model or deep neural network behind a user-facing application. The main challenge is serving predictions quickly and reliably as traffic grows, while keeping quality and cost under control.
What strategies would you use to serve an LLM or deep neural network at scale with low latency?
Inference optimization for transformer and neural network workloadsServing architecture choices such as routing, batching, and cachingTrade-offs between latency, quality, and infrastructure costHow to validate that optimizations do not degrade output quality