Can RTX 3070 Ti run Hunyuan3D 2?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Hunyuan3D 2 comfortably using the FP16 quantization, which fits in 16.0 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — insufficient VRAM in interactive use. Two-stage image-to-3D — shape generation then PBR texture synthesis. Strong topology.
How to run it
- 1. Install Ollama or LM Studio.
- 2. Pull the
FP16GGUF — best balance of quality and speed on 8 GB. - 3. Start chatting. Expect ~0 tok/sec on first-token, faster after warmup.
Other models that run great on RTX 3070 Ti
FAQ (20)
What GPU do I need to run Hunyuan3D 2?
To run Hunyuan3D 2, you need a GPU with at least 16 GB of VRAM. Higher-end GPUs like the RTX 3090 or A6000 are recommended for optimal performance.
Is Hunyuan3D 2 good for coding?
Hunyuan3D 2 is primarily designed for generating 3D models from images and is not optimized for coding tasks. For coding, consider language models like Codex or AlphaCode.
Hunyuan3D 2 vs Llama 3.1 8B?
Hunyuan3D 2 is specialized for 3D generation with 2.5B parameters, while Llama 3.1 8B is a larger, more general-purpose language model. Choose based on your specific task needs.
Can I run Hunyuan3D 2 on a Mac?
Yes, you can run Hunyuan3D 2 on a Mac with a compatible GPU that has at least 16 GB of VRAM. Ensure you have the necessary drivers and CUDA support installed.
How much VRAM does Hunyuan3D 2 need?
Hunyuan3D 2 requires 16 GB of VRAM. The exact amount can vary slightly depending on the quantization level used.
Is Hunyuan3D 2 censored?
Hunyuan3D 2 is not explicitly censored, but it adheres to standard content guidelines to ensure appropriate and safe use.
Is Hunyuan3D 2 commercial-use allowed?
Hunyuan3D 2 is licensed under the tencent-hunyuan license, which allows for commercial use. Check the license terms for any specific restrictions.
Hunyuan3D 2 context length?
The context length for Hunyuan3D 2 is currently unknown. It is designed for 3D generation tasks rather than text-based models, so context length may not be applicable in the same way.
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