Troubleshooting

GPU and Memory Issues

CUDA Out of Memory

Problem: Getting CUDA out of memory errors when loading or running models. Solutions:

Use Quantized Models
- FP8 quantization for H100/H200 GPUs (requires CUDA 12+)
- Check the HuggingFace collection for quantized versions

Adjust Precision

# Use bfloat16 instead of float32
model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct",
    dtype=torch.bfloat16,
    device_map="auto"
)

Enable Flash Attention 2

# Install flash-attn first
# pip install -U flash-attn --no-build-isolation

model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto"
)

Reduce Visual Token Budget

# For images - reduce max_pixels
processor.image_processor.size = {
    "longest_edge": 512*32*32,  # Reduced from default
    "shortest_edge": 256*32*32
}

# For videos - reduce frame count or resolution
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    fps=1  # Reduce from default fps=2
)

Use a Smaller Model
- Qwen3-VL-2B or 4B for edge/consumer GPUs
- Qwen3-VL-30B-A3B (MoE) has only 3B active parameters

Minimum VRAM Requirements

Actual memory usage is typically 1.2-1.5x the theoretical minimum due to activations and intermediate tensors.

Estimated VRAM by Model Size (BF16 precision):

Model	BF16 VRAM	INT8 VRAM	Notes
Qwen3-VL-2B	~4-5 GB	~2-3 GB	Suitable for consumer GPUs
Qwen3-VL-4B	~8-10 GB	~4-5 GB	RTX 3090/4090
Qwen3-VL-8B	~16-20 GB	~8-10 GB	A100 40GB, H100
Qwen3-VL-32B	~64-80 GB	~32-40 GB	Multi-GPU required
Qwen3-VL-30B-A3B	~60-75 GB	~30-38 GB	MoE model
Qwen3-VL-235B-A22B	~450-550 GB	~225-275 GB	8x H100 recommended

Multi-GPU Setup

Problem: Model doesn’t fit on a single GPU. Solution: Use tensor parallelism or model parallelism

# Automatic device mapping
model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-32B-Instruct",
    dtype=torch.bfloat16,
    device_map="auto"  # Automatically splits across GPUs
)

For vLLM:

vllm serve Qwen/Qwen3-VL-235B-A22B-Instruct-FP8 \
  --tensor-parallel-size 8 \
  --mm-encoder-tp-mode data \
  --enable-expert-parallel

Installation Issues

Transformers Version

Problem: Model loading fails or unexpected behavior. Solution: Ensure you have the correct transformers version:

# Qwen3-VL requires transformers >= 4.57.0
pip install "transformers>=4.57.0"

Flash Attention Installation

Problem: Flash Attention compilation fails. Solutions:

Check CUDA compatibility
- Flash Attention 2 requires CUDA 11.6+
- Check GPU compatibility (Ampere, Ada, Hopper architectures)

Install pre-built wheels

pip install flash-attn --no-build-isolation

Build from source (if wheels fail)

git clone https://github.com/Dao-AILab/flash-attention
cd flash-attention
python setup.py install

Video Processing Dependencies

Problem: Video loading fails or hangs. Solutions:

Install with video support

# Recommended: Use torchcodec (fastest, most compatible)
# See https://github.com/pytorch/torchcodec for installation

# Or use decord (Linux only from PyPI)
pip install qwen-vl-utils[decord]

# Fallback: torchvision (slowest but most compatible)
pip install qwen-vl-utils

Video URL compatibility
Backend HTTP HTTPS
torchvision >= 0.19.0 ✅ ✅
torchvision < 0.19.0 ❌ ❌
decord ✅ ❌
torchcodec ✅ ✅

Backend	HTTP	HTTPS
torchvision >= 0.19.0	✅	✅
torchvision < 0.19.0	❌	❌
decord	✅	❌
torchcodec	✅	✅

Force specific backend

export FORCE_QWENVL_VIDEO_READER=torchcodec  # or decord, torchvision

Context Length Issues

Input Too Long

Problem: Sequence length exceeds model’s context window. Default Context Length: 256K tokens Solutions:

Reduce Visual Tokens

# Reduce image resolution
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/image.jpg",
                "min_pixels": 50176,    # Lower resolution
                "max_pixels": 50176,
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

Enable YaRN for Extended Context (up to 1M tokens) Modify config.json:

{
    "max_position_embeddings": 1000000,
    "rope_scaling": {
        "rope_type": "yarn",
        "mrope_section": [24, 20, 20],
        "mrope_interleaved": true,
        "factor": 3.0,
        "original_max_position_embeddings": 262144
    }
}

For vLLM:

vllm serve Qwen/Qwen3-VL-8B-Instruct \
  --rope-scaling '{"rope_type":"yarn","factor":3.0,"original_max_position_embeddings":262144,"mrope_section":[24,20,20],"mrope_interleaved":true}' \
  --max-model-len 1000000

Because Interleaved-MRoPE’s position IDs grow more slowly than vanilla RoPE, use a smaller scaling factor. For 1M context with 256K base, use factor=2 or 3, not 4.

Video Too Long

Problem: Long videos exceed token budget. Solutions:

Reduce FPS

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    fps=1  # Lower FPS for longer videos
)

Set Frame Limit

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    num_frames=128,  # Maximum frames
    fps=None  # Overwrite fps
)

Use total_pixels limit with qwen-vl-utils

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "path/to/video.mp4",
                "total_pixels": 20480 * 32 * 32,  # Limit total tokens
            },
            {"type": "text", "text": "Describe this video."},
        ],
    }
]

Model Loading Issues

Download Errors

Problem: Model download fails or is very slow. Solutions:

For users in mainland China: Use ModelScope

from modelscope import snapshot_download

model_dir = snapshot_download('qwen/Qwen3-VL-8B-Instruct')

Resume interrupted downloads

from huggingface_hub import snapshot_download

snapshot_download(
    "Qwen/Qwen3-VL-8B-Instruct",
    resume_download=True
)

Use HF mirror (set environment variable)

export HF_ENDPOINT=https://hf-mirror.com

Import Errors

Problem: ImportError or ModuleNotFoundError. Solutions:

Check all dependencies

pip install transformers>=4.57.0 accelerate qwen-vl-utils

For vLLM
```
pip install vllm>=0.11.0
```

For web demo

pip install -r requirements_web_demo.txt

Inference Issues

Slow Inference

Problem: Generation is very slow. Solutions:

Use vLLM for production

vllm serve Qwen/Qwen3-VL-8B-Instruct \
  --host 0.0.0.0 \
  --port 8000

Enable Flash Attention

model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct",
    attn_implementation="flash_attention_2"
)

Use FP8 quantization (H100/H200)

vllm serve Qwen/Qwen3-VL-8B-Instruct-FP8

Batch inference

# Process multiple inputs together
processor.tokenizer.padding_side = 'left'
inputs = processor.apply_chat_template(
    [messages1, messages2, messages3],
    padding=True
)

Unexpected Outputs

Problem: Model generates incorrect or unexpected results. Solutions:

Check input format
- Verify image/video paths are correct
- Ensure proper message structure

Adjust generation parameters

# For more deterministic outputs
generated_ids = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.1,  # Lower temperature
    top_p=0.9,
    do_sample=True
)

Use appropriate model edition
- Instruct: General-purpose tasks
- Thinking: Complex reasoning, STEM, math

Verify processor settings

# Reset to defaults if customized
processor = AutoProcessor.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct"
)

Docker Issues

Container Won’t Start

Problem: Docker container fails to start. Solutions:

Check GPU availability

docker run --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi

Install NVIDIA Container Toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

Use official image

docker run --gpus all --ipc=host --network=host --rm \
  -it qwenllm/qwenvl:qwen3vl-cu128 bash

API Issues

API Authentication Errors

Problem: API calls fail with authentication errors. Solution: Set API key correctly

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_DASHSCOPE_API_KEY",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

See the API documentation for more details.

Getting Help

If you’re still experiencing issues:

Check the GitHub Issues: Qwen3-VL Issues
Join the Community:
- Discord
- WeChat Group
Consult the Documentation:

Additional Resources

GPU and Memory Issues

CUDA Out of Memory

Minimum VRAM Requirements

Multi-GPU Setup

Installation Issues

Transformers Version

Flash Attention Installation

Video Processing Dependencies

Context Length Issues

Input Too Long

Video Too Long

Model Loading Issues

Download Errors

Import Errors

Inference Issues

Slow Inference

Unexpected Outputs

Docker Issues

Container Won’t Start

API Issues

API Authentication Errors

Getting Help

Build docs developers (and LLMs) love

Additional Resources

Documentation Index

​GPU and Memory Issues

​CUDA Out of Memory

​Minimum VRAM Requirements

​Multi-GPU Setup

​Installation Issues

​Transformers Version

​Flash Attention Installation

​Video Processing Dependencies

​Context Length Issues

​Input Too Long

​Video Too Long

​Model Loading Issues

​Download Errors

​Import Errors

​Inference Issues

​Slow Inference

​Unexpected Outputs

​Docker Issues

​Container Won’t Start

​API Issues

​API Authentication Errors

​Getting Help

​Related Resources

Build docs developers (and LLMs) love

GPU and Memory Issues

CUDA Out of Memory

Minimum VRAM Requirements

Multi-GPU Setup

Installation Issues

Transformers Version

Flash Attention Installation

Video Processing Dependencies

Context Length Issues

Input Too Long

Video Too Long

Model Loading Issues

Download Errors

Import Errors

Inference Issues

Slow Inference

Unexpected Outputs

Docker Issues

Container Won’t Start

API Issues

API Authentication Errors

Getting Help

Related Resources