> For the complete documentation index, see [llms.txt](https://infronai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infronai.gitbook.io/docs/llm-inference-handbook/llm-inference-basics/training-vs.-inference.md).

# Training vs. Inference

### Training: Building the model’s understanding <a href="#training-building-the-models-understanding" id="training-building-the-models-understanding"></a>

Training occurs initially when building an LLM. It is about teaching the model how to recognize patterns and make accurate predictions. This is done by exposing the model to vast amounts of data and adjusting its parameters based on the data it encounters.

Common techniques used in LLM training include:

* **Supervised learning**: Show the model examples of inputs paired with the correct outputs.
* **Reinforcement learning**: Allow the model to learn by trial and error, optimizing based on feedback or rewards.
* **Self-supervised learning**: Learn by predicting missing or corrupted parts of the data, without explicit labels.

Training is computationally intensive, often requiring expensive GPU or TPU clusters. While this initial cost can be very high, it is more or less a one-time expense. Once the model achieves desired accuracy, retraining is usually only necessary to update or improve the model periodically.

### Inference: Using the model in real-time <a href="#inference-using-the-model-in-real-time" id="inference-using-the-model-in-real-time"></a>

LLM inference means applying the trained model to new data to make predictions. Unlike training, inference happens continuously and in real-time, responding immediately to user input or incoming data. It is the phase where the model is actively "in use." Better-trained and more finely-tuned models typically provide more accurate and useful inference.

Inference compute needs are ongoing and can become very high, especially as user interactions and traffic grow. Each inference request consumes computational resources such as GPUs. While each inference step may be smaller than training in isolation, the cumulative demand over time can lead to significant operational expenses.

Here is a side-by-side comparison between training and inference:

| Purpose    | Teach the model                   | Use the model                                        |
| ---------- | --------------------------------- | ---------------------------------------------------- |
| Data       | Huge datasets                     | New, user-provided inputs                            |
| Compute    | Long, expensive GPU/TPU jobs      | Real-time, repeated workloads                        |
| Cost model | Mostly one-time                   | Ongoing and scales with traffic                      |
| Hardware   | Multi-node clusters               | Smaller clusters, optimized runtimes and cache usage |
| Time       | Hours to weeks                    | Milliseconds to seconds                              |
| Tools      | PyTorch, JAX, DeepSpeed, Megatron | vLLM, SGLang, TensorRT-LLM, MAX, LMDeploy            |

### FAQs <a href="#faqs" id="faqs"></a>

#### Where do training and inference fit in the LLM lifecycle? <a href="#where-do-training-and-inference-fit-in-the-llm-lifecycle" id="where-do-training-and-inference-fit-in-the-llm-lifecycle"></a>

Training happens early in the lifecycle. The model learns patterns, language structure, and general knowledge. After that, the model goes through alignment and optional fine-tuning.&#x20;

Inference comes last. It’s the stage where the model is deployed and serves real users in production. You can think of training as “building the model” and inference as “putting the model to work.”

#### Why does LLM inference often cost more than training? <a href="#why-does-llm-inference-often-cost-more-than-training" id="why-does-llm-inference-often-cost-more-than-training"></a>

Even though training an LLM is expensive, it usually happens once. Inference, on the other hand, runs every time a user sends a request. As traffic grows, the number of inference calls grows with it. Each request uses GPU compute, memory, and network bandwidth. Over time, this ongoing demand can make inference the larger long-term expense, especially for applications with heavy usage or long prompts.

#### Should I train my own LLM? <a href="#should-i-train-my-own-llm" id="should-i-train-my-own-llm"></a>

In most cases, no. Training a new LLM from scratch requires massive datasets, specialized hardware, and a dedicated research team. Most companies get better results by starting with an existing open-source model and then fine-tuning or customizing it for their domain. Full training only makes sense if you’re solving a problem that existing models can’t handle or you have strict control requirements that fine-tuning can’t meet.

#### Is fine-tuning considered training or inference? <a href="#is-fine-tuning-considered-training-or-inference" id="is-fine-tuning-considered-training-or-inference"></a>

Fine-tuning is a form of training. You update some of the model’s weights using new data to adapt it to a specific task or domain. Inference doesn’t change any weights. It only uses the model to generate predictions. See the fine-tuning section to learn more.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://infronai.gitbook.io/docs/llm-inference-handbook/llm-inference-basics/training-vs.-inference.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.