When building applications that integrate with LLMs to support capabilities such as chatbots and agents capable of natural language communication and autonomous decision making, it’s important to have a clear understanding of how LLM inference runtime works. Inference refers to the process of using an already-trained model to produce an output—in this case, a token or a sequence of tokens. Without that understanding, it’s easy to waste time optimizing the wrong things.
The most critical fact is straightforward: inference time is linearly proportional to the number of tokens generated. One token means one runtime iteration. A thousand tokens means a thousand iterations. Each iteration applies the full neural network to the current context. That’s the loop.
There’s an upfront cost to process the input—tokenization and context encoding—which also scales with the number of input tokens. But it happens once. As output length increases, that cost diminishes into irrelevance. This gives a clean mental model: runtime ≈ output token count.
Another unintuitive point: the “difficulty” of the prompt doesn’t matter. A trivial question takes the same time to answer as a deeply technical one—if the output length is the same. The model doesn’t “think harder” about complex prompts. It just applies the same function to the same shape of data.
Also worth knowing: the entire input is passed in as a single tensor and processed in parallel across every layer. The model doesn’t read tokens one at a time—it ingests the full sequence, distributes it across GPUs and nodes on a network, and applies every layer to the full context. Every layer processes the entire input context window.
So to recap: inference time scales with the number of tokens generated. Input length adds a one-time cost that quickly becomes irrelevant for longer outputs. The complexity of the prompt doesn’t matter. Each output token is produced by a constant-time application of the full model to the entire context. That context is passed in all at once, distributed across GPUs and networked nodes, and processed in parallel across every layer.
What are some other unintuitive LLM facts that apply in day-to-day work?
Note from the AI: This explanation reflects typical behavior for modern transformer-based decoder models used in API-accessible LLMs. It assumes inference with caching enabled and distributed execution at scale. Performance characteristics may differ in edge deployments, non-transformer architectures, or environments without caching. Within those bounds, the simplifications above offer a reliable guide.