Getting random numbers from a truncated Maxwell-Boltzmann distribution

I would like to generate random numbers using a truncated Maxwell-Boltzmann distribution. I know that scipy has a built-in Maxwell random variables, but there is no truncated version of it (I am also aware of a truncated normal distribution, which is irrelevant here). I have tried to write my own random variables using rvs_continuous:

class maxwell_boltzmann_pdf(st.rv_continuous):
def _pdf(self,x):
    n_0 = np.power(np.pi,3/2)*np.square(v_0)*(v_0*erf(v_esc/v_0)-(2/np.sqrt(np.pi))*v_esc*np.exp(-np.square(v_esc/v_0)))
    return (1/n_0)*(4*np.pi*np.square(x))*np.exp(-np.square(x/v_0))*np.heaviside(v_esc-x,0)

maxwell_boltzmann_cv = maxwell_boltzmann_pdf(a=0, b=v_esc, name='maxwell_boltzmann_pdf')

This does exactly what I want, but it is way too slow for my purpose (I am doing Monte Carlo simulations), even if I draw all the random velocities outside of all the loops. I have also thought of using Inverse transform sampling method, but the inverse of the CDF does not have an analytic form and I will need to do a bisection for every number I draw. It would be great if there is a convenient way for me to generate random numbers from a truncated Maxwell-Boltzmann distribution with decent speed.