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Trellis2.c – Local 3D generation with Vulkan and CUDA

by wimaxs·Jul 16, 2026·1 point·0 comments

AI Analysis

●●●BangerWizardryBig BrainZero to One

Porting Microsoft's 4B-param 3D model to pure C++ with Vulkan support is a massive engineering flex.

Strengths
  • Eliminates Python/PyTorch runtime dependency for a complex 4B-parameter diffusion model.
  • Implements dual backends (CUDA and Vulkan) enabling cross-vendor GPU support including AMD and Intel.
  • Bundles a complete pipeline: image-to-3D, texturing, decomposition, and rigging in one toolkit.
Weaknesses
  • Inference performance is currently unoptimized and slower than mature PyTorch implementations.
  • Vulkan backend stability on non-NVIDIA hardware remains unproven and needs community testing.
Category
Target Audience

Graphics programmers, AI engineers, and developers needing offline 3D asset generation

Similar To

llama.cpp · stable-diffusion.cpp · TRELLIS

Post Description

Hi HN, I started this because I wanted something in the spirit of llama.cpp and stable-diffusion.cpp, but for local 3D generation model: native executables, no Python/PyTorch runtime, and both CUDA and Vulkan backends. I’ve mainly tested it on an NVIDIA RTX 4090. I’m particularly interested in Vulkan results on AMD and Intel GPUs, where driver behavior and memory allocation may differ. If you try it, GPU, driver, OS details, and failure logs would be extremely helpful. The main limitation right now is performance. The implementation has not yet been heavily optimized, so inference is currently slower than mature, fully optimized PyTorch or Vulkan backends. There is still substantial room for kernel, memory-use, and scheduling improvements.

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