Determining the amount of memory an AI agent requires for optimal performance is one of the fundamental challenges for developers. Depending on model complexity, input data volume, and the number of learning parameters, memory requirements can range from a few hundred megabytes to several gigabytes.
Recent research shows that using model compression techniques, memory mapping, and computational load distribution can significantly reduce RAM consumption while preserving the agent's efficiency. Therefore, planners should conduct memory load tests before deployment and apply appropriate configurations based on the results.