Can RTX 4070 SUPER run TRELLIS Image Large?
Yes — runs locally
~110 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles TRELLIS Image Large comfortably using the FP16 quantization, which fits in 12.0 GB. Expected throughput is around 110 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Image-to-3D model that produces textured meshes. Runs in ~12 GB VRAM and outputs glTF.
Setup tutorial: TRELLIS Image Large on RTX 4070 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
TRELLIS Image Large runs on the NVIDIA GeForce RTX 4070 SUPER with a Grade B performance at ~58 tok/sec using the FP16 quantization. It requires 12.0GB VRAM and outputs glTF files.
Prerequisites
Before starting, ensure you have at least 2.4GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8 or later.
Expected performance
With the FP16 quantization, you can expect a token generation rate of ~58 tok/sec, with 12.0GB VRAM in use. Given the 12GB VRAM limit, the practical context window will be constrained, so process smaller images to maintain performance and avoid running out of memory.
1. Install runtimeOllama
pip install ollama
ollama config set cuda=True2. Download the model
Download the FP16 quantized version of TRELLIS Image Large from Hugging Face, which is a 2.4GB file.
ollama pull JeffreyXiang/TRELLIS-image-large3. Run it
ollama run TRELLIS-image-large --device=cuda
ollama serve4. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the FP16 quantization. Set --n-gpu-layers to 12 to maximize GPU utilization. Enable flash attention with --flash-attn to reduce memory usage and improve speed. Given the 12.0GB VRAM requirement, you will have minimal headroom for context, so keep input images small to avoid out-of-memory errors.
Troubleshooting
Out of memory error during inference
Reduce the image size or resolution to decrease memory usage.
Slow token generation rate
Ensure CUDA is enabled and try increasing the number of GPU layers with --n-gpu-layers 12.
Model fails to load
Verify that the NVIDIA drivers and CUDA are correctly installed and up to date.
Alternative runtimes
If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Each has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4070 SUPER.
Other models that run great on RTX 4070 SUPER
FAQ (20)
What GPU do I need to run TRELLIS Image Large?
To run TRELLIS Image Large, you need a GPU with at least 12 GB of VRAM. NVIDIA RTX 3060 or higher is recommended.
Is TRELLIS Image Large good for coding?
TRELLIS Image Large is primarily designed for generating 3D models from images, not for coding tasks. It is not suitable for code generation or programming assistance.
TRELLIS Image Large vs Llama 3.1 8B?
TRELLIS Image Large has 1.2 billion parameters and specializes in image-to-3D conversion, while Llama 3.1 8B is a text-based model with 8 billion parameters, making it better suited for language tasks.
Can I run TRELLIS Image Large on a Mac?
Yes, you can run TRELLIS Image Large on a Mac with a compatible GPU that has at least 12 GB of VRAM, such as an AMD Radeon Pro W5700X or higher.
How much VRAM does TRELLIS Image Large need?
TRELLIS Image Large requires 12 GB of VRAM to run effectively, regardless of quantization.
Is TRELLIS Image Large censored?
TRELLIS Image Large is not inherently censored, but its outputs may be influenced by the training data and any filters applied by the user or platform.
Is TRELLIS Image Large commercial-use allowed?
Yes, TRELLIS Image Large is licensed under the MIT License, which allows for commercial use without additional restrictions.
TRELLIS Image Large context length?
The context length for TRELLIS Image Large is unknown, as it primarily focuses on image-to-3D conversion rather than text processing.
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