I told it:

In here, it says that maximum memory is 10240mb, so that may not work:

Memory and computing power

Memory is the principal lever available to Lambda developers for controlling the performance of a function. You can configure the amount of memory allocated to a Lambda function, between 128 MB and 10,240 MB. The Lambda console defaults new functions to the smallest setting and many developers also choose 128 MB for their functions.

Its response:

ChatGPT: I apologize for the confusion earlier. You are correct that the maximum memory allocation for a Lambda function is 10,240 MB. Here is the revised CloudFormation YAML template with 10,240 MB memory allocation:

```yaml
Resources:
LambdaFunction:
Type: AWS::Lambda::Function
Properties:
FunctionName: LambdaWith10GBMemory
Handler: index.handler
Runtime: nodejs14.x
MemorySize: 10240
Timeout: 300
Role: !GetAtt 'LambdaRole.Arn'
Code:
S3Bucket: <S3_BUCKET_NAME>
S3Key: <S3_KEY>

LambdaRole:
Type: AWS::IAM::Role
Properties:
RoleName: LambdaRoleWith10GBMemory
AssumeRolePolicyDocument:
Version: '2012-10-17'
Statement:
- Effect: Allow
Principal:
Service:
- lambda.amazonaws.com
Action:
- sts:AssumeRole
Policies:
- PolicyName: LambdaBasicExecution
PolicyDocument:
Version: '2012-10-17'
Statement:
- Effect: Allow
Action:
- logs:CreateLogGroup
- logs:CreateLogStream
- logs:PutLogEvents
Resource: arn:aws:logs:*:*:*

```

Replace <S3_BUCKET_NAME> and <S3_KEY> with the appropriate values for your S3 bucket and object key that contain your Lambda function code. You may also adjust Runtime, Handler, and Timeout as needed.

===

Then I said:

What can we do to be able to use 16gb memory?

Then it said:

ChatGPT: To use 16 GB memory in a serverless context, you will need to consider other options as AWS Lambda currently supports up to 10,240 MB. Two alternatives are AWS Fargate or running a container with Amazon ECS.

Here's an example of using AWS Fargate with a task definition for a container with 16 GB of memory:

1. Create an ECS cluster:

aws ecs create-cluster --cluster-name MyFargateCluster

2. Create a task definition with 16 GB of memory (you can adjust the values as needed). Save the JSON below to a file called fargate-task-definition.json:

{
"family": "FargateTaskWith16GBMemory",
"taskRoleArn": "<YOUR_TASK_ROLE_ARN>",
"executionRoleArn": "<YOUR_EXECUTION_ROLE_ARN>",
"networkMode": "awsvpc",
"containerDefinitions": [
{
"name": "MyContainer",
"image": "<YOUR_DOCKER_IMAGE>",
"cpu": 4096,
"memory": 16384,
"memoryReservation": 16384,
"essential": true,
"portMappings": [
{
"containerPort": 80,
"hostPort": 80,
"protocol": "tcp"
}
]
}
],
"requiresCompatibilities": [
"FARGATE"
],
"cpu": "4096",
"memory": "16384"
}

Replace <YOUR_TASK_ROLE_ARN>, <YOUR_EXECUTION_ROLE_ARN> and <YOUR_DOCKER_IMAGE> with appropriate values for your use case.

3. Register the task definition:

aws ecs register-task-definition --cli-input-json file://fargate-task-definition.json

4. Launch the Fargate task:

aws ecs run-task --cluster MyFargateCluster --launch-type FARGATE --task-definition FargateTaskWith16GBMemory --network-configuration "awsvpcConfiguration={subnets=[<YOUR_SUBNET_ID>],securityGroups=[<YOUR_SECURITY_GROUP_ID>]}"

Replace <YOUR_SUBNET_ID> and <YOUR_SECURITY_GROUP_ID> with the appropriate values for your VPC and security group.
This will give you a Fargate container running with 16 GB of memory, and you can adjust the resources allocated to the container as needed.

====

I think that's quite a nice answer.