One of the very useful techniques in Image Processing is the 2D Gaussian Filter, especially when smoothing images. However, the implementation of a 2D Gaussian Filter requires heavy computational resources, and when it comes down to real-time applications, efficiency in the implementation is vital. Floating-point math represents an obstacle for this, as its implementation requires a large amount of computational power in order to achieve real-time image processing. On the other hand, a fixed-point approach is much more suitable; implementation of a 2D Gaussian Filter in FPGA using fixed-point arithmetic provides efficiency in the processing and reduction in computational costs. The purpose of this study is to present the FPGA resource usage for different sizes of Gaussian Kernel; to provide a comparison between fixed-point and floating point implementations; and to define the amount of bits are necessary to use in order to have a Root Mean Square Error (RMSE) below 5%.