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In [15]: import numpy as np
    ...: 
    ...: size = (10_000, 15)
    ...: X = np.random.normal(size=size)
    ...: %timeit func(X)  # 1.28 s
    ...: %timeit func_numba(X)  # 2.05 s
144 ms ± 823 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)
181 ms ± 1.62 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [16]: import numpy as np
    ...: 
    ...: size = (10_000, 15)
    ...: X = np.random.normal(size=size)
    ...: %timeit func(X)  # 1.28 s
    ...: %timeit func_numba(X)  # 2.05 s
143 ms ± 960 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)
93.9 ms ± 332 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [17]: import numpy as np
    ...: 
    ...: size = (10_000, 15)
    ...: X = np.random.normal(size=size)
    ...: %timeit func(X)  # 1.28 s
    ...: %timeit func_numba(X)  # 2.05 s
145 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
203 ms ± 1.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)