How to speed up Blenderbot inference with Sagemaker?
When I hit the inference API for theBlenderbot-400M-distillmodel, I get a response within 3 seconds. However, when I try to deploy the model on AWS Sagemaker, my prediction times range to about 10 seconds irrespective of whether I use a compute-optimized instance or a memory-optimized one.from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'facebook/blenderbot-400M-distill',
	'HF_TASK':'conversational'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m4.4xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': {
		"past_user_inputs": ["Which movie is the best ?"],
		"generated_responses": ["It's Die Hard for sure."],
		"text": "Can you explain why ?"
	}
})I have even tried this withml.c5.4xlargeandml.m6g.4xlargeinstances. The predicition time doesn’t improve.Any suggestions would be really appreciated!Reference:Amazon SageMaker Pricing - Machine Learning - Amazon Web Services