Outsourcing ML training to cloud-service-providers presents a compelling opportunity for resource constrained clients, while it simultaneously bears inherent privacy risks. We introduce Silenuio, the first fully non-interactive outsourcing scheme for the training of MLPs that achieves 128bit security using FHE (precisely TFHE). Unlike traditional MPC-based protocols that necessitate interactive communication between the client and server(s) or non-collusion assumptions among multiple servers, Silenzio enables the "fire-and-forget" paradigm without such assumptions. In this approach, the client encrypts the training data once, and the server performs the training without any further interaction. Silenzio operates entirely over low-bitwidth integer to mitigate the computational overhead inherent to FHE. Our approach features a novel low-bitwidth matrix multiplication gadget that leverages input-dependent residue number systems, ensuring that no intermediate value overflows 8bit. Starting from an RNS-to-MRNS conversion process, we propose an efficient block-scaling mechanism, which approximately shifts encrypted tensor values to their user-specified most significant bits. To instantiate the backpropagation of the error, Silenzio introduces a low-bitwidth gradient computation for the cross-entropy loss. We evaluate Silenzio on standard MLP training tasks regarding runtime as well as model performance and achieve similar classification accuracy as MLPs trained using PyTorch with 32bit floating-point computations. Our open-source implementation of Silenzio represents a significant advancement in privacy-preserving ML, providing a new baseline for secure and non-interactive outsourced MLP training.