Run HunyuanImage 3.0 Open Source Locally

A new image generation model, HunyuanImage-3.0, has been released as open source. Benchmarks indicate strong performance, close to several top-ranked models. The example images also look promising in terms of quality.

In this guide, I show how to run HunyuanImage-3.0 locally, what kind of hardware it needs, and what to expect in terms of VRAM usage and speed. I tested it on a cloud machine because the VRAM requirements are high.

What is HunyuanImage-3.0?

HunyuanImage-3.0 is an open-source image generation model. It aims for high-quality outputs while keeping local inference possible for those with sufficient GPU memory. Early results from benchmarks and sample images suggest it performs well across common prompts.

The project includes a local demo that exposes a simple interface for text-to-image generation. Installation follows the official repository’s instructions and can be done from the command line.

Overview at a Glance

Item	Details
Model	HunyuanImage-3.0
Type	Image generation (text-to-image)
Availability	Open source
Demo	Included; chat-style UI (runs locally)
Test OS	Linux
Test GPUs	3× NVIDIA H100 (80 GB each)
Total VRAM on test machine	240 GB
Observed VRAM usage	~97–98% per GPU (3-GPU setup)
Steps run	50
Time for 50 steps	2 minutes 17 seconds
Speed per iteration	~2.74 seconds
Stability on 3 GPUs	Risk of out-of-memory at completion
Practical recommendation	Use at least 4 GPUs to be safe

Key Features of HunyuanImage-3.0

• Open-source release with local inference support.
• Strong benchmark results and promising image quality.
• Chat-based local demo for interactive prompting.
• Straightforward installation from the repository.
• Mixture-of-experts design influences multi-GPU activity patterns.

Test Environment and Hardware

I rented a cloud machine to run the model because the VRAM footprint is high. Here is the setup I used.

Hardware I used

• Operating system: Linux
• GPUs: 3× NVIDIA H100
• VRAM per GPU: 80 GB
• Total VRAM: 240 GB

This configuration sits near the lower bound for stable local runs with the default settings used in my tests. VRAM headroom was very tight.

Why VRAM matters here

• The model allocates large tensors across multiple GPUs.
• The demo run with 50 steps approached the VRAM limit on all three GPUs (97–98%).
• With this memory pressure, an out-of-memory error can occur at or near the end of a run.

Installation and Setup

The repository provides a clear set of commands to install dependencies and launch the local demo. I followed it line by line on the command line.

Step-by-step guide

1. Prepare your system

   • Install a recent version of Python (and CUDA/cuDNN if required by the repo).
   • Ensure GPU drivers are installed and visible to your deep learning framework.

2. Get the code

   • Clone the official repository for HunyuanImage-3.0.
   • Switch into the project directory.

3. Create an isolated environment

   • Create a virtual environment or Conda environment.
   • Activate the environment.

4. Install dependencies

   • Install Python packages listed in the repository (requirements file or setup instructions).
   • Confirm that GPU acceleration is available (e.g., check that your framework detects the GPUs).

5. Download model assets

   • Follow the repository’s instructions to obtain model weights or checkpoints.
   • Place files where the repository expects them.

6. Launch the demo

   • Run the provided script to start the local demo server.
   • Open the URL shown in the terminal to access the interface.

Notes on the included demo

• The repository includes a Gradio-based demo.
• It presents a chat-style interface for entering prompts and generating images.
• You can run it locally and interact with the model in a browser.

Demo Interface and Workflow

The demo’s interface is chat-based. You enter a text prompt and submit it, and the model responds with an image. The UI is simple and focused on the prompt box and output area.

How I ran a prompt

• Entered a text prompt into the chat box.
• Clicked submit to start the generation.
• Watched progress indicators in the terminal.
• Monitored GPU and CPU activity in the system monitor at the bottom of the screen.

What I observed during generation

• All three GPUs became very busy.
• The model uses a mixture-of-experts design; not all parts seem to run at the same time.
• GPU activity appeared to alternate as the model moved through steps.

VRAM Usage and Practical Headroom

On a 3×H100 (80 GB each) setup, VRAM usage was around 97–98% per GPU during a 50-step run. That is effectively at capacity.

Recommendation based on usage

• With 3 GPUs, you are at the limit and can hit an out-of-memory error.
• I recommend at least 4 GPUs for headroom and more stable runs.

Why add a fourth GPU

• Reduces the chance of out-of-memory at step completion.
• Provides space for the model’s peak memory usage during later stages of generation.
• Leaves room for any overhead from the UI, logging, and framework internals.

Performance: Time and Speed

The 50-step test completed in 2 minutes and 17 seconds, with a reported iteration speed of about 2.74 seconds per step. This gives you a baseline for planning local runs.

Comparison with other models

• Compared with models like Flux and Qwen-Image, the speed here is not very fast.
• The difference likely comes from architectural choices; HunyuanImage-3.0 is a different design.

Expectation setting

• I expect performance improvements as optimization work progresses.
• For now, plan for multi-minute runs at this step count, especially under high VRAM pressure.

Failure Case: Out-of-Memory After 50 Steps

A run completed the 50 steps but then triggered an out-of-memory error. This shows how close the memory budget is with three 80 GB GPUs.

What this means for setup

• You can complete a full set of steps and still fail at the end due to a last memory spike.
• A fourth GPU is a practical safeguard on similar configs.

My takeaway for stability

• Aim for more VRAM than the bare minimum reported during generation.
• Treat 3×80 GB as risky territory for default runs.

Practical Workflow: From Prompt to Image

Here is the streamlined process I followed to generate images locally.

Step-by-step prompt workflow

1. Start the demo server from the repository.
2. Open the local URL in your browser.
3. Enter a text prompt in the chat box.
4. Click submit to begin the generation.
5. Watch the terminal progress bar for step updates.
6. Observe GPU and CPU monitors to track system load.
7. Save or inspect the generated image once it appears.

Tips for smoother runs

• Keep background GPU tasks to a minimum.
• Avoid running other heavy processes on the same machine.
• If you see VRAM nearing 100% on any GPU, consider reducing steps or scaling hardware.

Monitoring GPU Load

During runs, I monitored GPU and CPU usage. The three GPUs stayed highly active, with VRAM close to full.

How to track system load

• Use system tools or the framework’s logging to view GPU and CPU usage.
• Watch for memory spikes close to the end of the run.
• Consider logging step-by-step timings to understand bottlenecks.

Interpreting GPU activity

• A mixture-of-experts design can lead to shifting loads across GPUs.
• Not all components need to be active on every step.
• Alternating activity patterns are normal and not a problem on their own.

Recommendations for Local Runs

Based on the tests, here is what I recommend for running HunyuanImage-3.0 locally with fewer issues.

Minimum practical setup

• At least 4 high-memory GPUs for default settings.
• Stable drivers and a consistent environment for CUDA and your ML framework.

Run-time settings

• Keep step count reasonable if on tight VRAM.
• Consider smaller settings only if you must stay on 3 GPUs.

Operational checks

• Confirm that each GPU is detected before launching the demo.
• Watch VRAM usage from the first steps to catch a potential failure early.
• If you see memory errors, add hardware headroom or reduce load.

Quick Reference: Observed Metrics

Metric	Observation
GPUs used	3× H100 (80 GB)
VRAM load	~97–98% per GPU
Steps	50
Total time	2 minutes 17 seconds
Speed per iteration	~2.74 seconds
Stability	Out-of-memory possible at completion
Suggested GPUs	At least 4 for safer runs

Final Thoughts

HunyuanImage-3.0 is open source and shows strong results on benchmarks and sample images. Running it locally is straightforward if you follow the repository’s instructions, and the included Gradio demo makes prompting easy.

In my tests on a Linux machine with 3× H100 GPUs (80 GB each), VRAM use stayed near 97–98% per GPU during a 50-step run. One run completed the steps but failed with an out-of-memory error at the end. Based on this, I recommend at least four GPUs to provide enough headroom for stable local inference.

If you have the hardware, the setup is simple: install dependencies from the repository, launch the demo, enter your prompt, and monitor progress in the terminal. Expect around 2 minutes and 17 seconds for 50 steps at roughly 2.74 seconds per iteration on a configuration similar to mine. With additional GPU memory, you can reduce the risk of out-of-memory errors and maintain a smoother experience.