> ## Documentation Index
> Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Is Too Large To Fit In Memory

## Observed Error

```
Model is too large to fit in memory. This can happen because of a large batch size, large input tensor dimensions, or other network parameters. Please refer to the Troubleshooting section in the documentation for potential workarounds
```

## Causes and Possible Solutions

The memory requirements of your model are too large to fit on the device. Potential workarounds include:

* On transformer models, please compile again with the batch size set to 1 using one CS-2 system to determine if the specified maximum sequence length is feasible.

* You can try a smaller batch size per device or enable batch tiling (only on transformer models) by setting the `micro_batch_size` parameter in the `train_input` or `eval_input` section of your model’s yaml file (see working\_with\_microbatches). \* If you ran with batch tiling with a specific `micro_batch_size` value, you can try compiling with a decreased `micro_batch_size`. The [Using “explore” to Search for a Near-Optimal Microbatch Size](../../model-zoo/tutorials/optimize-performance-with-automatic-microbatching#using-explore-to-search-for-a-near-optimal-microbatch-size) flow can recommend performant micro batch sizes that will fit in memory.

* On CNN models where batch tiling isn’t supported, try manually decreasing the batch size and/or the image/volume size.

<Info>
  Note

  For more information on working with batch tiling and selecting performant `micro_batch_size` values, visit working\_with\_microbatches
</Info>

<Info>
  Note

  The `batch_size` parameter set on the yaml configuration is the **global batch size**. This means that the batch size per CS-2 system is computed as the global batch size divided by the number of CS-2s used.
</Info>