Can RTX 3090 Ti run TRELLIS Image Large?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles TRELLIS Image Large comfortably using the FP16 quantization, which fits in 12.0 GB. Expected throughput is around 132 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run TRELLIS Image Large on an NVIDIA GeForce RTX 3090 Ti with FP16 quantization for Grade S performance at ~116 tok/sec.
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 512.15 or later) installed along with CUDA 11.4 or higher.
Expected performance
With the FP16 quantization, you can expect the model to run at approximately 116 tokens per second, utilizing around 12.0GB of VRAM. Given the remaining 12.0GB of VRAM, you can achieve a practical context window of several thousand tokens, depending on the complexity of the input images.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the FP16 quantized version of TRELLIS Image Large (2.4GB file) from Hugging Face.
ollama pull JeffreyXiang/TRELLIS-image-large3. Run it
ollama run TRELLIS-image-large
ollama interactive TRELLIS-image-large4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU, enable flash attention (--flash-attn) for faster inference, and consider using tensor parallelism (--tensor-parallel-size 2) to distribute the workload across multiple GPUs if available. With 24GB VRAM, you can allocate up to 12GB for the model, leaving ample headroom for context.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers or decrease the batch size.
Slow inference speed
Enable flash attention (--flash-attn) and ensure CUDA is properly configured.
Model fails to load
Verify the model file integrity and ensure the Ollama runtime is correctly installed and configured.
Alternative runtimes
Alternatively, you can use LM Studio for a more user-friendly interface, llama.cpp for lower-level control and customization, or Jan for integrated development environments. Choose Ollama for its ease of use and CUDA backend support, especially for high-performance inference on the RTX 3090 Ti.
Other models that run great on RTX 3090 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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