This work introduces an Additive Manufacturing (AM) Finite Element (FE) framework for the large-scale thermo-mechanical modeling of metal parts. Due to the complexity of the physical phenomenon and to meet the demands of the industry for time-to-market, an accurate and reliable simulation tool able to operate in a high-performance computer is required. The framework is based on a scalable and efficient octree-based library; a parallel search for the evolving domain and a multicriteria refinement/coarsening strategy [1, 2] to preserve the accuracy of the solution and keep the number of FEs controlled during the simulation. The AM pipeline is analyzed via a set of experiments aiming at evaluating its performance under different scenarios. In this regard, numerical simulations involving growing domains are presented to evaluate the computational efficiency of the numerical solution in different octree-based Adaptive Mesh Refinement (AMR) strategies and a reference fixed mesh. Next, the strong scalability of the FE framework is evaluated considering all the steps of the AM pipeline: the element activation, refinement operations and thermo-mechanical solvers. The performance of the framework is assessed over a discrete evolving domain with over 13M Degrees of Freedom (DOFs) solved on a High-Performance Computer (HPC) using 2,048 processors. Acknowledgments The financial support from the Ministry of Science and Innovation (MCIN) via: the PriMuS project (Printing pattern based and MultiScale enhanced performance analysis of advanced Additive Manufacturing components, ref. num. PID2020-115575RB-I00, MCIN/ AEI/10.13039/501100011033/). References [1] J. Baiges, M. Chiumenti, C. A. Moreira, M. Cervera, and R. Codina. An adaptive finite element strategy for the numerical simulation of additive manufacturing processes. Additive Manufacturing, 37:101650, 2021. [2] C. A. Moreira, M. A. Caicedo, M. Cervera, M. Chiumenti, and J. Baiges. A multi-criteria h-adaptive finite-element framework for industrial part-scale thermal analysis in additive manufacturing processes. Engineering with Computers, 2022.