As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern.
While Autoregressive (AR) models are the industry standard, Diffusion Language Models (DLLMs) have emerged as a promising alternative capable of parallel token generation.
In this paper
We perform a comparative energy benchmark of diffusion models (LLaDA-8B, Dream-7B) against AR counterparts (Llama-3.1-8B, Qwen-2.5-7B).
We analyze the trade-offs between model architecture, KV-cache optimization, and Joules-per-token efficiency.
Our results show that at longer sequences (200--600 tokens), diffusion models achieve 2.5--2.7 J/token, approximately 2$\times$ more efficient than AR models with KV-cache (5.3--5.7 J/token).
KV-cache provides substantial benefits for AR models at longer sequences (50--70\% energy reduction).