> For the complete documentation index, see [llms.txt](https://docs.everinfer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everinfer.ai/examples/bert-with-zero-overhead.md).

# BERT With Zero Overhead

In this tutorial we're going to demonstrate how you can deploy one of the fastest NLP models, BERT, to remote GPUs and maintain decent latency.

## Why BERT?

BERT encoder is extremely fast, running at 1.5ms on local GPU (tested on Nvidia T4). Deploying that model to remote machines and maintaining low latency is hard. 

Everinfer is highly optimized and will allow you to run that model on remote machines while keeping up with its speed. 

## How to deploy BERT on Everinfer

Install Everinfer and HuggingFace transformers library.

Convert the model to ONNX format:

```
!python3 -m transformers.onnx --model=distilbert-base-uncased onnx/
```

Authenticate on Everinfer using your API key, upload the model, and create inference engine:

```python
from everinfer import Client
client = Client('my_api_key')
pipeline = client.register_pipeline('bert', ['onnx/model.onnx'])
runner = client.create_engine(pipeline['uuid'])
```

You are ready to go!

Since HuggingFace tokenizers are fully compatible with Everinfer expected input format, you can feed tokenizer outputs directly to the deployed model:

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
inputs = tokenizer("Everinfer is fast af", return_tensors="np")
```

After applying tokenizer to input text, running the model is as simple as:

```python
preds = runner.predict([inputs]) 
```

## Performance 

Remote GPU access overhead is virtually zero! 

<figure><img src="/files/KrIMERIKbAxsvNx094H9" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
You could deploy that code to AWS Lambda to go fully serverless, or use it as a part of your self-hosted web app.&#x20;
{% endhint %}


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