Please recommend various option values to upload 100 million data to qdrant.

Currently, I am engaged in a job related to natural language processing. The current qdrant can include the embedded vector value through various option values.
But I keep getting the error "broken pipe" when I put in 100 million embedded vectors. The parallel value is 4.
I use the upload_collection function when I put 100 million embedded vector data into qdrant. I would like to get a recommendation from you for various option values of this function.
I would also like to hear an approximate answer about how long it takes to put in 100 million data. I look forward to your good reply.

[vector info]

1. Number of embedding vectors: 100 million
2. embedding vector dimension : 768

[hardware spec]

1.cpu core : 10 core
2.cpu : Intel(R) Core(TM) i9-10900X
3.ram Capacity: 251 GiB (Free: 106 GiB)

[upload_collection function Option value]

1. payload = None
2. parallel : 4

[collection info]

1. distance: Cosine
2. shard_number : 5
3. memmap_threshold : 1,000,000,000
4. indexing_threshold : 100,000,000

No source file or test is named. Start by reading the Python client's upload_collection entry point and its option documentation, then examine the reported 100-million-vector configuration and broken-pipe failure. Done would require a documented, reproducible recommendation for the options and an evidence-based upload-time estimate.