TimeGPT can concurrently forecast many series at the same time. Simply use a DataFrame with more than one unique value in the unique_id column. 
import pandas as pd
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)
👍 Use an Azure AI endpoint To use an Azure AI endpoint, remember to set also the base_url argument: nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key") 
# Read the data
df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')

# Forecast
forecast_df = nixtla_client.forecast(
    df=df, 
    h=24
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: H
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to set model="azureai": nixtla_client.forecast(..., model="azureai") For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon. By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.
For more details on forecasting multiple series, read our in-depth tutorial on Multiple series forecasting.