What Actually Happens When You Call an LLM API?

You write something simple like this: response = client . responses . create ( model = " gpt-4o " , input = " Explain backpressure in simple terms " ) A few hundred milliseconds later, text begins streaming back. It feels instant. It feels simple. But that single API call triggers a surprisingly complex distributed system involving: Global traffic routing Authentication and token-based quota enforcement Multi-tenant scheduling GPU memory management Continuous batching Autoregressive token decoding Streaming transport over persistent connections An LLM API is not just “a model running on a server.” It is a real-time scheduling and resource allocation system built on top of extremely expensive hardware. Under the hood, your request is competing with thousands of others for: GPU compute GPU memory Context window capacity Batch slots Network bandwidth Understanding this pipeline changes how you think about: Latency Rate limiting Prompt size Streaming Retries System reliability In this arti