IO bottleneck when hashing: How many threads are best?

I want to hash all files in a directory. IO (to external SSD drive) is the bottleneck. How many threads should I use? Does it even matter?

import hashlib

def hash_path(path: str):
    h = hashlib.md5()
    with open(path, 'rb') as f:
        for chunk in iter(lambda: f.read(4096), b''):
            h.update(chunk)
    return h.hexdigest()

from pathlib import Path
path = Path('/media/usb/dynamic_docs')
paths = [p for p in path.glob('**/*') if p.is_file()]
paths = paths[:1000]

from multiprocessing.pool import ThreadPool 
from tqdm.notebook import tqdm

x = [1,2,3,4,6,8,10,15,20,25,30,40,50,70,80,120,140]
y = []
for n in tqdm(x):
with ThreadPool(n) as pool:
    start = timeit.default_timer()
    pool.map(hash_path, paths)
    end = timeit.default_timer()
    duration = end - start
    print(n, duration)
    y.append(duration)

1 5.8160336340006324
2 3.622440821993223
3 3.1050608700024895
4 2.9156927020012517
6 2.775160079996567
8 2.7484407580050174
10 2.7938896600026055
15 2.8651001560065197
20 2.8214209449943155
25 2.7490319029966486
30 2.8025731050001923
40 2.8451450540014775
50 2.6865460660046665
70 2.8644705260012415
80 2.8269265680064564
120 2.73746527700132
140 2.775528302998282