Fastest way to get exact count of rows for a 100GB CSV file stored on S3

What is the fastest way of getting an exact count of rows for a 100GB CSV file stored on Amazon S3 without using Athena nor any Fargate or EC2 VM? I can’t use Athena, because the CSV file isn’t clean-enough for it. I can’t use Fargates or EC2 VMs, because I need a purely serverless solution. I can’t use third-party services like Snowflake (native AWS services only).

Also, 100GB is too large to fit within a Lambda Function‘s /tmp (limited to 10GB). I could try to run something like DuckDB (or any other streaming database engine) on a Lambda and scan the entire file with a SELECT COUNT(*) FROM "s3://myBucket/myFile.csv" query, but the Lambda is quite likely to timeout, because its read bandwidth from S3 is 100MB/s at best, and it cannot run for more than 15 minutes (900s).

I know the approximate size of the file.

Note: I have an inaccurate estimate of the number of rows provided by AWS Glue Data Catalog‘s crawler, with an error margin of -50%/+100%. This could be used for some kind of iterative or dichotomous process, but I could not figure any out. For example, I tried adding an OFFSET with a value lower than but close to the number of rows to the aforementioned query, but the Lambda running DuckDB timed out. That was disappointing and somewhat surprising, because a query like SELECT * FROM "s3://myBucket/myFile.csv" LIMIT 10 OFFSET 10000000 worked well.