Skip to main content

What is LLM inference?

LLM inference refers to using trained LLMs, such as GPT-4, Llama 4, and DeepSeek-V3, to generate meaningful outputs from user inputs, typically provided as natural language prompts. During inference, the model processes the prompt through its vast set of parameters to generate responses like text, code snippets, summaries, and translations.

Essentially, this is the moment the LLM is actively "in action." Here are some real-world examples:

  • Customer support chatbots: Generating personalized, contextually relevant replies to customer queries in real-time.
  • Writing assistants: Completing sentences, correcting grammar, or summarizing long documents.
  • Developer tools: Converting natural language descriptions into executable code.
  • AI agents: Performing complex, multi-step reasoning and decision-making processes autonomously.

Why should I care about LLM inference?​

You might think: I’m just using OpenAI’s API. Do I really need to understand inference?

Serverless APIs like OpenAI, Anthropic, and others make inference look simple. You send a prompt, get a response, and pay by the token. The infrastructure, model optimization, and scaling are all hidden from view.

But here’s the thing: the further you go, the more inference matters.

As your application grows, you'll eventually run into limits (e.g., cost, latency, customization, or compliance) that serverless APIs can’t fully address. That’s when teams start exploring hybrid or self-hosted solutions.

Understanding LLM inference early gives you a clear edge. It helps you make smarter choices, avoid surprises, and build more scalable systems.

  • If you're a developer or engineer: Inference is becoming as fundamental as databases or APIs in modern AI application development. Knowing how it works helps you design faster, cheaper, and more reliable systems. Poor inference implementation can lead to slow response time, high compute costs, and a poor user experience.
  • If you're a technical leader: Inference efficiency directly affects your bottom line. A poorly optimized setup can cost 10× more in GPU hours while delivering worse performance. Understanding inference helps you evaluate vendors, make build-vs-buy decisions, and set realistic performance goals for your team.
  • If you're just curious about AI: Inference is where the magic happens. Knowing how it works helps you separate AI hype from reality and makes you a more informed consumer and contributor to AI discussions.

For more information, see serverless vs. self-hosted LLM inference.