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Quick Tour

The easiest way of getting started is using the official Docker container. Install Docker following their installation instructions.

Let’s say you want to deploy Falcon-7B Instruct model with TGI. Here is an example on how to do that:

volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data --model-id $model

To use GPUs, you need to install the NVIDIA Container Toolkit . We also recommend using NVIDIA drivers with CUDA version 11.8 or higher.

To use TGI on RoCm-enabled AMD GPUs (only MI210 and MI250 are tested), please use the image instead. For details about the usage on RoCm, please refer to the Supported Hardware section and AMD documentation.

Once TGI is running, you can use the generate endpoint by doing requests. To learn more about how to query the endpoints, check the Consuming TGI section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.

import requests

headers = {
    "Content-Type": "application/json",

data = {
    'inputs': 'What is Deep Learning?',
    'parameters': {
        'max_new_tokens': 20,

response ='', headers=headers, json=data)
# {'generated_text': '\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can'}

To see all possible deploy flags and options, you can use the --help flag. It’s possible to configure the number of shards, quantization, generation parameters, and more.

docker run --help