Elementary reactions possess characteristic times ranging multiple or-ders of magnitude, resulting into a stiff ODE system requiring specialized solvers. Popular Python libraries such as Scipy lead to an intractable computational cost for kinetic simulations on huge CRNs and/or stiff ODEs. Therefore, CARE additionally implements the Julia solvers DifferentialEquations.jl and DiffEqGPU.jl . These outperform Scipy solvers and are prepared to be run on GPUs, providing a speed-up of ×20-100 compared to their CPU counterparts. CARE employs the backward differentiation formula (BDF) as default ODE solver (BDF in Scipy, FBDF in Julia). 64-bit floating-point arithmetic is implemented for microkinetic simulations on both CPU and GPU (although the latter is typically optimized for 32-bit floating-point).