throughput

Batch scoring a billion items is a throughput-and-cost problem, the opposite of low-latency serving. Every choice that hurts latency helps you here. Here is how to design for dollars-per-million-documents.

Knowing the algorithms is half the job. The other half is the serving stack that actually delivers throughput inside a latency budget. The signal is matching the server to the workload and naming the four knobs that move the needle.

Continuous batching is the single biggest throughput lever in modern LLM serving. The signal is explaining why static batching idles the GPU on variable-length generation, and why PagedAttention is what makes the fix practical.

LLM inference has two phases with opposite resource profiles, and co-locating them makes a long prompt stall everyone else's tokens. The signal is knowing why prefill and decode fight, and what separating them costs. Here is the answer.

nvidia-smi showing 100% can hide that you are using a fraction of the hardware's real compute. The signal is MFU (useful FLOPs vs peak) and the gap between 'the GPU is busy' and 'the GPU is efficient'.

CPU-based autoscaling that works fine for a web tier quietly fails on GPU inference: wrong signal, and replicas that take minutes to warm. The interviewer wants the signals you actually scale on and how you hide the cold start.

Three batching strategies, three very different latency profiles. Picking wrong leaves throughput or tail latency on the floor. Here is what each one costs and when to use it.