Can RTX 5090 run TRELLIS Image Large?
Yes — runs locally
~216 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles TRELLIS Image Large comfortably using the FP16 quantization, which fits in 12.0 GB. Expected throughput is around 216 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run TRELLIS Image Large on an NVIDIA GeForce RTX 5090 with FP16 quantization for Grade S performance at ~155 tok/sec.
Prerequisites
Before starting, ensure you have at least 5GB 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 the model to run at ~155 tok/sec, using 12.0GB of VRAM. The remaining 20.0GB of VRAM provides ample headroom to handle larger context windows, allowing for more complex image-to-3D conversions without running out of memory.
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 --quant=FP16 --n-gpu-layers=128 --flash-attn
ollama interactive4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, set --n-gpu-layers to 128 to fully utilize the GPU memory. Enable --flash-attn for faster attention computation. With 12.0GB VRAM used by the model, you will have approximately 20.0GB of VRAM headroom for larger context windows and additional tasks.
Troubleshooting
Out of Memory (OOM) errors during inference.
Reduce the number of --n-gpu-layers or decrease the batch size.
Slow inference speed.
Ensure --flash-attn is enabled and check if your CUDA installation is up to date.
Model fails to load.
Verify the model file integrity and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Each runtime has its strengths, but Ollama is optimized for ease of use and performance on high-end GPUs like the RTX 5090.
Other models that run great on RTX 5090
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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