Tensor networks are a class of algorithms aimed at reducing the computational complexity of high-dimensional problems. They are used in an increasing number of applications, from quantum simulations to machine learning. Exploiting data parallelism in these algorithms is key to using modern hardware. However, there are several ways to map required tensor operations onto linear algebra routines (“building blocks”). Optimizing this mapping impacts the numerical behavior, so computational and numerical aspects must be considered hand-in-hand. In this paper we discuss the performance of solvers for low-rank linear systems in the tensor-train format (also known as matrix-product states). We consider three popular algorithms: TT-GMRES, MALS, and AMEn. We illustrate their computational complexity based on the example of discretizing a simple high-dimensional PDE in, for example, 50¹⁰ grid points. This shows that the projection to smaller sub-problems for MALS and AMEn reduces the number of floating-point operations by orders of magnitude. We suggest optimizations regarding orthogonalization steps, singular value decompositions, and tensor contractions. In addition, we propose a generic preconditioner based on a TT-rank-1 approximation of the linear operator. Overall, we obtain roughly a 5× speedup over the reference algorithm for the fastest method (AMEn) on a current multicore CPU.