Can RTX 4070 Ti SUPER run TRELLIS Image Large?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti SUPER (16 GB VRAM) handles TRELLIS Image Large comfortably using the FP16 quantization, which fits in 12.0 GB. Expected throughput is around 144 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 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
The TRELLIS Image Large model runs at Grade A performance on the NVIDIA GeForce RTX 4070 Ti SUPER with FP16 quantization, achieving ~77 tok/sec.
Prerequisites
Before starting, ensure you have at least 5GB of free disk space, a compatible operating system (Windows 10/11 or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the recommended FP16 quantization, you can expect the model to run at approximately 77 tokens per second, utilizing about 12.0GB of VRAM. The remaining 4.0GB of VRAM provides sufficient headroom for handling larger context windows, allowing for more complex image-to-3D transformations.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the FP16 quantized version of TRELLIS Image Large (2.4GB) from Hugging Face.
ollama pull JeffreyXiang/TRELLIS-image-large3. Run it
ollama run TRELLIS-image-large --device cuda
ollama interact TRELLIS-image-large4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU, enable flash attention (--flash-attn) to reduce memory usage, and consider using tensor parallelism (--tensor-parallel-size 2) to distribute the workload across multiple GPUs if available. This configuration will help maintain the ~77 tok/sec speed while keeping VRAM usage around 12.0GB, leaving 4.0GB for context and other tasks.
Troubleshooting
Out of Memory (OOM) errors during inference
Reduce the number of GPU layers (--n-gpu-layers) or disable flash attention (--no-flash-attn) to lower VRAM usage.
Slow inference times
Ensure CUDA is properly installed and the correct device is selected with --device cuda. Consider increasing the tensor parallelism (--tensor-parallel-size 2) if you have multiple GPUs.
Model fails to load
Verify the model file integrity by re-downloading it using the command: ollama pull JeffreyXiang/TRELLIS-image-large
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment. LM Studio is ideal for a graphical interface, llama.cpp offers more fine-grained control over optimizations, and Jan is suitable for lightweight, embedded systems. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 4070 Ti SUPER.
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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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