Single thread CPU Linear algebra is the bottleneck of most small systems, so if you can't do that right, you are going to have problems. If you don't believe th

Single thread CPU Linear algebra is the bottleneck of most small systems, so if you can't do that right, you are going to have problems. If you don't believe the benchmark, feel free to run them yourself. That said, Jax also has bigger issues in it's handling of higher derivatives. Currently, it only supports a few types of jacobians, and the ones it is missing include all the sparse methods that can make your code orders of magnitude faster. https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html#composing-vjps-jvps-and-vmap https://jax.readthedocs.io/en/latest/notebooks/autodiff_cook.... DifferentialEquations, on the other hand can do automatic sparsity detection https://diffeq.sciml.ai/stable/tutorials/advanced_ode_example/#Declaring-a-Sparse-Jacobian https://diffeq.sciml.ai/stable/tutorials/advanced_ode_exampl....