[Python] Warm up Hugging Face transformer models efficiently to reduce first-token latency in...

In production deployment of Hugging Face LLMs, the first inference call often has very high latency ("cold start"), even on a machine where the model is already loaded into memory.

Subsequent calls are much faster.

I want to implement a model warm-up strategy that:

1. Primes the model and GPU memory before real user requests arrive
2. Reduces first-token generation time for users
3. Works for both pipeline()-based and model.generate()-based inference

from transformers import pipeline

generator = pipeline('text-generation', model="tiiuae/falcon-7b-instruct", device=0)

def generate_text(prompt):
return generator(prompt, max_new_tokens=50)[0]['generated_text']


My Question:

What is the best way to warm up a Hugging Face transformer model after loading, to minimize first-token latency in production inference?

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