Model reference · open weights
HunyuanImage-3.0 is an open-weight image model from tencent. HunyuanImage-3.0-Instruct (BF16) weighs 169 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the tencent/HunyuanImage-3.0-Instruct model card, 2026-10-01
What it is
| Released by | tencent |
|---|---|
| Released | 2025-09-25 |
| Parameters | 83.0B |
| VRAM | 169 GB for the weights |
What it runs on
| Card | One image | Memory |
|---|---|---|
| RTX 3060 12 GB | tight (encoders offloaded) | 11.6 GB |
| RTX 4060 Ti 16 GB | fits (encoders offloaded) | 15.4 GB |
| RTX 3090 24 GB | fits (encoders offloaded) | 23.4 GB |
| RTX 4090 24 GB | fits (encoders offloaded) | 23.4 GB |
| RTX 5090 32 GB | fits (encoders offloaded) | 31.0 GB |
| L40S 48 GB | fits (encoders offloaded) | 44.0 GB |
| A100 80 GB | fits (encoders offloaded) | 78.2 GB |
| H100 80 GB | fits (encoders offloaded) | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits (encoders offloaded) | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits (encoders offloaded) | 107 GB |
| H200 141 GB | fits (encoders offloaded) | 138 GB |
| B200 180 GB | tight | 176 GB |
From the model card
💻 Official website(官网) Try our model!  
If you develop/use HunyuanImage-3.0 in your projects, welcome to let us know.
HunyuanImage-3.0 is a groundbreaking native multimodal model that unifies multimodal understanding and generation within an autoregressive framework. Our text-to-image and image-to-image model achieves performance comparable to or surpassing leading closed-source models.
🧠 Unified Multimodal Architecture: Moving beyond the prevalent DiT-based architectures, HunyuanImage-3.0 employs a unified autoregressive framework. This design enables a more direct and integrated modeling of text and image modalities, leading to surprisingly effective and contextually rich image generation.
🏆 The Largest Image Generation MoE Model: This is the largest open-source image generation Mixture of Experts (MoE) model to date. It features 64 experts and a total of 80 billion parameters, with 13 billion activated per token, significantly enhancing its capacity and performance.
🎨 Superior Image Generation Performance: Through rigorous dataset curation and advanced reinforcement learning post-training, we've achieved an optimal balance between semantic accuracy and visual excellence. The model demonstrates exceptional prompt adherence while delivering photorealistic imagery with stunning aesthetic quality and fine-grained details.
💭 Intelligent Image Understanding and World-Knowledge Reasoning: The unified multimodal architecture endows HunyuanImage-3.0 with powerful reasoning capabilities. It under stands user's input image, and leverages its extensive world knowledge to intelligently interpret user intent, automatically elaborating on sparse prompts with contextually appropriate details to produce superior, more complete visual outputs.
# 1. First install PyTorch (CUDA 12.8 Version)
pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
# 2. Install tQuoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel, load the workflow from the model's card on Hugging Face, and choose this file in its text encoder loader.
# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the model (5.0 GB)
get tencent/HunyuanImage-3.0-Instruct model-0002-of-0032.safetensors text_encoders
start-comfyui# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT