The aim of this paper is to assess the feasibility and on-board hardware performance requirements for on-board telemetry forecasting by implementing a Recurrent Neural Network (RNN) on low-cost multicore RISC-V microprocessor. Gravity field and steady-state Ocean Circulation Explorer (GOCE) public telemetry data was used for training RNNs with different hyperparameters and architectures. The prediction accuracy of these models was evaluated using mean error and R-squared score on the same test dataset. The implementation of the RNN on a RISC-V embedded device, representative of future space-grade hardware, required some adaptations and modifications due to the computational requirements and the large memory footprint. The algorithm was implemented to run in parallel on the 8 cores of the microprocessor and tiling was employed for the weight matrices. Further considerations have also been made for the approximation of sigmoid and hyperbolic tangent as activation functions.@en