Without looking at the code, O(N * k) with N = 9000 points and k = 50 dimensions should take in the order of milliseconds, not seconds. Did you profile your code to see whether there is perhaps something that takes an unexpected amount of time?

The '2 seconds' figure comes from the end-to-end time on a standard laptop. I quoted 2s to set realistic expectations for the user experience, not the CPU cycle count. You are right that the core linear algebra (Ax=b) is milliseconds. The bottleneck is the DOM/rendering overhead, but strictly speaking, the math itself is blazing fast.

Thats not how big-O notation works. You don’t know what proportionality constants are being hidden by the notation so you cant make any assertions about absolute runtimes

If he wrote the for loop in python instead of numpy or C or whatever it could be a plausible runtime.

Each of the N data points is processed through several expensive linear algebra operations. O(N * k) just expresses that if you double N, the runtime also at most doubles. It doesn't mean it has to be fast in an absolute sense for any particular value of N and k.