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Hunyuan Video Generation Error on Consumer GPU: Fix Guide

May 15, 2026 · Editorial Team · 6 min read · hunyuan-videotroubleshootingerror-fix

You've cloned the Hunyuan Video repo, set up the conda environment, downloaded the model weights, and run the sample command from the README. Instead of a generated video, you get a wall of red text. Maybe it's CUDA out of memory. Maybe it's RuntimeError: CUDA error: device-side assert triggered. Maybe the script starts, gets to the first sampling step, and then Python just terminates with an exit code. If you're running this on a consumer GPU (RTX 3090, 4090, or anything with under 24GB VRAM), you've entered a well-documented pain zone for Hunyuan Video's local installation.

Hunyuan Video is a capable open-source model, but its default configuration assumes server-grade hardware. Consumer GPU users can absolutely run it, but not with the stock settings. Here's what's failing and how to fix it.

What this error actually means

Hunyuan Video's base model requires approximately 60GB of VRAM to run at full precision (FP32) for a standard generation. That's not a typo. The full model is enormous, designed to run on A100 or H100 clusters. Consumer GPUs top out at 24GB (RTX 4090) or less.

The fix isn't to get different hardware. The fix is to run the model in quantized form with CPU offloading enabled. Hunyuan Video supports BF16 and INT8 quantization modes that reduce VRAM requirements significantly, and it supports offloading transformer blocks to CPU RAM when GPU VRAM is exhausted. With the right config, an RTX 4090 can run Hunyuan Video. An RTX 3090 can run it more slowly. An RTX 3080 with 10GB can run it in a degraded but functional configuration.

The errors you're seeing are almost always VRAM-related, not code bugs. The model is trying to load more data onto your GPU than it can hold.

Quick fix (when you need it working in 60 seconds)

  1. Add --bf16 to your generation command if you're not already using it. This halves the memory footprint versus FP32. Example: python sample_video.py --bf16 --prompt "your prompt here"
  2. Add --cpu-offload to enable transformer CPU offloading. This pushes overflowing layers to system RAM. You need at least 32GB of system RAM for this to work. Example: python sample_video.py --bf16 --cpu-offload --prompt "your prompt here"
  3. Reduce the number of inference steps: add --num-inference-steps 30. The default is 50. Lower steps use less peak VRAM and are significantly faster, at some quality cost.
  4. Reduce video resolution to 480p: add --width 848 --height 480. The memory requirement drops quadratically with resolution.
  5. Close all other GPU-intensive applications before running. Chrome with GPU acceleration, Discord, other model servers: all of these consume VRAM.

Why this happens

The core issue is that Hunyuan Video's default configuration is documented for server hardware, and the error messages it produces when running on consumer hardware are often cryptic about the actual cause.

CUDA out of memory is the most honest error. It means exactly what it says: your GPU ran out of VRAM mid-generation. This typically happens at the start of the attention computation in the transformer blocks, which is when VRAM demand peaks.

device-side assert triggered is more opaque. In Hunyuan Video's context, this usually means a tensor shape mismatch caused by a memory allocation that partially failed before the assertion check. It's a downstream symptom of near-VRAM exhaustion, not a different root cause.

Python silently exiting (exit code 1 or 139) on consumer GPUs often means a segmentation fault in the CUDA driver triggered by out-of-memory conditions. The OS kills the process before Python can print a useful error.

Driver version mismatches are a secondary cause. Hunyuan Video's CUDA kernel implementations expect CUDA 12.1 or higher. Users running CUDA 11.x or outdated NVIDIA drivers see errors that look like general CUDA failures but are actually version compatibility problems.

The conda environment can also be the culprit. Hunyuan's requirements.txt specifies exact package versions. If you installed dependencies with a different pip or conda version, you may have partial version mismatches in torch, diffusers, or transformers that cause subtle runtime errors that don't clearly identify themselves.

Permanent fix

  1. Confirm your CUDA version: run nvidia-smi and check the CUDA version in the top right. You need 12.1 or higher. If you're on an older version, update your NVIDIA drivers from nvidia.com/drivers.
  2. Rebuild your conda environment cleanly: 
    conda create -n hunyuan python=3.10 -y
conda activate hunyuan
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
  3. Use the BF16 + CPU offload + reduced steps configuration as your baseline for consumer GPUs: 
    python sample_video.py \
  --bf16 \
  --cpu-offload \
  --num-inference-steps 30 \
  --width 848 \
  --height 480 \
  --num-frames 65 \
  --prompt "your prompt"
  4. If you have 24GB VRAM (RTX 4090), you can increase resolution to 720p: --width 1280 --height 720. Test at 480p first to confirm the baseline works.
  5. If you have less than 16GB VRAM, also add --use-flash-attn. Flash attention reduces attention block memory usage significantly. It requires a GPU with compute capability 8.0 or higher (RTX 30xx and above).
  6. Disable xformers if you have it installed and it's causing conflicts: set XFORMERS_DISABLED=1 as an environment variable before running.
  7. Monitor VRAM usage during generation with watch -n 1 nvidia-smi in a second terminal. This lets you see exactly when the OOM happens and tune your parameters accordingly.
  8. For INT8 quantization (deepest VRAM savings, lower quality): install bitsandbytes and add --quantize int8. This can run Hunyuan Video on GPUs with as little as 12GB VRAM, though generation quality is noticeably reduced.

Prevention

Keep your conda environment consistent and don't update packages in place. Hunyuan Video's dependency graph is fragile. When Tencent releases a new version of the model or config, clone fresh rather than pulling into an existing environment.

Pin your CUDA version. Updating your GPU drivers mid-project can shift your CUDA version and break working installations. Only update drivers when you're prepared to re-test and potentially re-install your PyTorch build.

Document the exact command that works for your hardware. Include VRAM usage at peak, generation time, and output quality notes. This becomes your reference for reproducing successful generations and diagnosing regressions when something changes.

Watch Hunyuan's GitHub releases page. Tencent has been actively improving the memory efficiency of the model throughout early 2026. A release from a month after your initial install may support lower VRAM configurations or have better consumer GPU defaults.

When the fix doesn't work

If you've correctly set up BF16 + CPU offload + reduced resolution and you're still getting CUDA OOM errors, your system RAM may be the bottleneck. CPU offloading moves data to RAM, not to disk. If your system only has 16GB RAM, the CPU offloading will exhaust RAM as quickly as VRAM, and the process will be killed by the OS.

The minimum for CPU offloading to work reliably is 32GB RAM. 64GB RAM allows full CPU offloading for 720p generation. If you're below 32GB RAM, running Hunyuan Video locally may not be feasible on your current hardware.

In that case, consider using Hunyuan Video through a cloud provider: RunPod, Vast.ai, and Modal all support it. Renting an A100 for a batch generation session is significantly cheaper than upgrading your local hardware for occasional use.

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