Fast-dLLM provides a family of techniques to accelerate diffusion-based LLMs and VLMs during inference. It implements training-free acceleration via KV cache reuse and parallel decoding strategies. The approach applies to text-only dLLMs, vision-language models (dVLMs), and vision-language-action models for autonomous driving, enabling faster generation without model fine-tuning.