Can RTX 5070 Ti run TRELLIS Image Large?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 Ti (16 GB VRAM) handles TRELLIS Image Large comfortably using the FP16 quantization, which fits in 12.0 GB. Expected throughput is around 156 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run TRELLIS Image Large on a NVIDIA GeForce RTX 5070 Ti with FP16 quantization for Grade A performance at ~77 tok/sec. Requires 12.0GB VRAM, leaving 4.0GB headroom.
Prerequisites
Before starting, ensure you have at least 2.4GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.85.12 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, expect the model to run at ~77 tok/sec, using approximately 12.0GB of VRAM, leaving 4.0GB of headroom for context. This should provide a snappy and responsive experience for most tasks.
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 --model=TRELLIS-image-large --device=cuda
ollama interactive4. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 12 to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 12.0GB VRAM requirement, you will have 4.0GB of headroom for context, allowing for a practical context window of several hundred tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers (--n-gpu-layers) or enable flash attention (--flash-attn) to lower VRAM usage.
Slow inference speed
Ensure CUDA is properly installed and update your NVIDIA drivers to the latest version. Consider enabling tensor parallelism if you have multiple GPUs.
Model fails to load
Verify the model file integrity and try downloading it again. Ensure you have the required disk space and permissions.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio is ideal for GUI-based model management, llama.cpp offers more control over quantization and optimization, and Jan is suitable for distributed training setups. However, Ollama provides a simpler and more streamlined experience for most users.
Other models that run great on RTX 5070 Ti
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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