GPU Inference

What it is

GPU inference is the production serving path for trained models. Inputs are batched, executed on GPUs, and turned into predictions, rankings, embeddings, or generated tokens.

Why it matters

GPUs are expensive and scarce. Good inference systems balance latency, throughput, quality, and utilization so model features can run at product scale.

How it works

Requests enter a scheduler, get batched with compatible work, run through optimized kernels, and return results. LLM serving also manages token streaming and KV cache memory.

When to use it

Use GPU inference for LLMs, recommendations, vision, speech, ranking, embeddings, and other models too heavy for CPUs.

Tradeoffs / failure modes

Large batches improve throughput but hurt latency. Small batches feel fast but waste GPU capacity. Memory pressure and queueing can create sharp tail-latency spikes.

Tiny example

ChatGPT streams tokens from a GPU cluster while a scheduler continuously batches active conversations to keep GPUs busy.

Related guides and systems

See Recommendation Engine, ChatGPT, YouTube, Gmail, and Google Photos.