Impact of GPU Architecture and VRAM on Image Generation: A Study of Energy Efficiency in Heterogeneous Edge Nodes
TL;DR
These findings demonstrate that contemporary generative workloads can be effectively supported by decentralized edge infrastructures, providing practical insights for the design of energy-efficient and heterogeneous local AI systems.
Abstract
The rapid evolution of generative artificial intelligence has substantially increased the computational demands of image synthesis models, traditionally restricting their execution to centralized cloud infrastructures. In response to concerns related to data privacy, energy consumption, cost, and dependency on hyperscale providers, this work investigates the feasibility of executing state-of-the-art generative image models at the network. We present a quantitative performance evaluation of two representative model families, Stable Diffusion XL (SDXL) and Z-Image Turbo, executed on heterogeneous hardware, including high-end consumer GPUs, mobile-class devices, and legacy workstation GPUs from NVIDIA and AMD. The analysis focuses on latency, power consumption, resource utilization, and the impact of software stack optimizations, such as attention mechanisms and backend frameworks, under realistic hardware constraints. Results show that software-level optimizations are the primary factors determining inference viability, often outweighing raw computational throughput. While modern GPUs benefit from optimized attention mechanisms and improved energy efficiency, legacy and lower-power devices remain viable when combined with optimized runtimes and model compression techniques. These findings demonstrate that contemporary generative workloads can be effectively supported by decentralized edge infrastructures, providing practical insights for the design of energy-efficient and heterogeneous local AI systems.