How do you design a scalable LLM inference system?

A scalable LLM inference system uses batching, caching, distributed serving, and load balancing across GPUs.

Scalability in LLM inference requires horizontal scaling of model servers, dynamic batching of requests, KV caching, and GPU optimization. Systems often use orchestrators to route requests based on load and latency targets.