A unified memory architecture provides one shared addressable memory pool for different processors in a system. This can reduce copying between CPU and GPU memory and allow either processor to work with a larger portion of the available capacity.
Large unified memory is useful for local AI because model weights, context and intermediate values can occupy substantial space. Capacity alone does not determine speed, however. Memory bandwidth, compute throughput, software support and model quantization also shape practical performance.
