Requirement Summary:

NLP Lambda function with alarge pre-trained model

Lambda layer became8.7 GB→ Exceeds AWS limits

Function returns RequestEntityTooLargeException

Need:High-performing,portable, lowinitialization time

Important AWS Limits:

Lambda Layers size limit(combined across all layers):250 MB (unzipped)

Deployment package size(unzipped):250 MB

Lambdacontainer image supportallows up to10 GBimage size

Evaluate Options:

A: Store model in S3 and load during execution

❌Leads tocold start latencyevery time

Model loading from S3 isslowerand not suitable for real-time NLP

Not optimal for performance

B: Use EFS mounted to Lambda

⚠️Valid for large models, butadds latencyduring cold start as model loads from EFS

Requires EFS setup, VPC, and has addednetwork I/O overhead

Still slower than bundling in container image

C: Split into five Lambda layers

❌Still violates thetotal layer size limitof 250 MB (unzipped)

You cannot exceed that even with multiple layers

✅D: Use Docker container image

✅Allows bundlingup to 10 GBof dependencies and models

✅Highportabilityandperformance

✅Avoids latency of downloading models at runtime

✅Ideal for scientific/NLP models

Lambda container image support:https://docs.aws.amazon.com/lambda/latest/dg/images-create.html

Lambda limits:https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html

Using large models with Lambda:https://aws.amazon.com/blogs/machine-learning/deploying-large-machine-learning-models-on-aws-lambda-with-container-images/