Gaussian Belief Propagation (GBP) is an iterative method for factor graph inference that provides an approximate solution to the probability distribution of a system. It has been shown to be a powerful tool in numerous applications including SLAM, where the estimation of the robot's position and the map of the environment is required. State-of-the-art implementations suffer from scalability issues, or exhibit performance degradation when off-chip memory access is required. This paper addresses these challenges using a streaming architecture via a chain of parameterizable Processing Elements (PE) that can be tuned to the problem's characteristics through the use of an optimizer. This work overcomes the limitations of existing GBP implementations achieving 142x-168x performance improvements over an embed-ded CPU for large graphs.