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3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vectset Diffusion Model (VDM). While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles at high-speed generation. Challenges exist because of not only difficulties in accelerating diffusion sampling but also VAE decoding in VDM -- areas under-explored in previous works. To address these challenges, we present FlashVDM, a systematic framework for accelerating both VAE and DiT in VDM. For DiT, FlashVDM enables flexible diffusion sampling with as few as 5 inference steps, while maintaining comparable quality, which is made possible by stabilizing consistency distillation with our newly introduced Progressive Flow Distillation technique. For VAE, we introduce a lightning vectset decoder equipped with Adaptive KV Selection, Hierarchical Volume Decoding,, and Efficient Network Design. By exploiting the locality of vectset and the sparsity of shape surface in the volume, the proposed decoder drastically lowers FLOPs, minimizing the overall decoding overhead. We apply FlashVDM to the current state-of-the-art open-source shape generation model Hunyuan3D-2, resulting in Hunyuan3D-2 Turbo. Through systematic evaluation for both generation and reconstruction, we demonstrate that our model outperforms existing fast 3D generation methods by a significant margin, achieving comparable performance to the state-of-the-art models while reducing inference time by over 45x for reconstruction and 32x for generation. Code and models are publicly available at https://github.com/Tencent-Hunyuan/FlashVDM.