How long does it take to pretrain an LLM?

There is no fixed amount of time required to pretrain an LLM.

Pretraining can take anywhere from days to many weeks or longer, depending on the model size, amount of training data, hardware, and training efficiency.

What determines pretraining time?

The biggest factors include:

• Number of model parameters
• Number of training tokens
• Number and type of GPUs or accelerators
• Batch size
• Sequence length
• Training software efficiency
• Network speed between machines
• Checkpointing and evaluation frequency
• Hardware failures or training interruptions

Why do larger models usually take longer?

Larger models require more computation for each training step.

They are also often trained on more tokens.

Conceptually:

more parameters
      +
more training tokens
      ↓
more total computation
      ↓
longer training unless more hardware is added

How does hardware affect the time?

Pretraining is usually distributed across many accelerators.

Using more hardware can reduce wall-clock time because more computation is performed in parallel.

However, scaling is not perfect.

As the number of machines grows, communication between devices becomes more expensive, so doubling the hardware does not always cut training time exactly in half.

What is the difference between compute and wall-clock time?

These are related but different.

Compute measures how much total processing work the training requires.

Wall-clock time is how long the training run actually takes in real time.

For example, a very large training job might require enormous total compute but finish sooner if it is distributed across a large cluster.

Does the amount of data matter?

Yes.

If a model is trained on more tokens, it usually requires more optimization steps or larger batches.

So training time depends not only on the number of parameters but also on how much data the model is expected to process.

Can training stop early?

Yes.

Training may stop because:

• The planned token budget is reached
• The loss stops improving enough
• The compute budget is exhausted
• A checkpoint is selected before the full schedule finishes
• The training strategy changes

The training duration is therefore usually planned around a compute and token budget rather than a universal number of days.

Is pretraining the only time-consuming stage?

No.

A complete model-development pipeline can also include:

• Data collection and cleaning
• Tokenization and preprocessing
• Evaluation
• Fine-tuning
• Preference training
• Safety testing
• Deployment optimization

So the total development time can be much longer than the pretraining run itself.

In short: pretraining time depends on model size, token count, hardware, and system efficiency. Small models may train relatively quickly, while very large LLMs can require long-running distributed training jobs across large accelerator clusters.