> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Variants

> Overview of Qwen3-VL model sizes, architectures, and editions

Qwen3-VL is available in multiple sizes and configurations to meet different deployment scenarios, from edge devices to cloud infrastructure.

## Model Sizes

Qwen3-VL comes in five different parameter scales:

### Dense Architecture Models

| Model Size | Parameters | Use Case | Hardware Requirement |
| - | - | - | - |
| **2B** | 2 Billion | Edge devices, mobile | Consumer GPUs |
| **4B** | 4 Billion | Lightweight deployment | Single GPU |
| **8B** | 8 Billion | Balanced performance | Single GPU (16GB+) |
| **32B** | 32 Billion | High-performance tasks | Multi-GPU |

### Mixture-of-Experts (MoE) Architecture

| Model Size | Total Parameters | Active Parameters | Use Case |
| - | - | - | - |
| **30B-A3B** | 30 Billion | 3 Billion | Efficient large-scale |
| **235B-A22B** | 235 Billion | 22 Billion | State-of-the-art performance |

> [!NOTE]
>
> MoE models activate only a subset of parameters per inference, providing better efficiency and performance tradeoffs compared to dense models of similar total parameter count.

## Model Editions

Each model size is available in two editions:

### Instruct Edition

**Optimized for direct task execution**

* Fast inference and response generation
* Suitable for production deployments
* Optimized for instruction-following
* Lower computational overhead

**Example**: `Qwen3-VL-8B-Instruct`

### Thinking Edition

**Enhanced reasoning with explicit thought processes**

* Provides step-by-step reasoning
* Better performance on complex tasks
* Useful for debugging and interpretability
* Longer output sequences

**Example**: `Qwen3-VL-8B-Thinking`

> [!TIP]
>
> Thinking editions are particularly effective for:
>
> * STEM and mathematical reasoning
> * Complex spatial understanding
> * Multi-step problem solving
> * Tasks requiring causal analysis

## Architecture Comparison

### Dense vs MoE

**Dense Models**

* All parameters active during inference
* Predictable memory usage
* Simpler deployment
* Best for: Resource-constrained environments

**MoE Models**

* Sparse activation patterns
* Higher capacity with lower compute
* Requires expert parallelism support
* Best for: Maximum performance scenarios

## Available Models

### Released Models (HuggingFace)

> **2B Models**
>
> * [Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct)
> * [Qwen3-VL-2B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking)

> **4B Models**
>
> * [Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct)
> * [Qwen3-VL-4B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking)

> **8B Models**
>
> * [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)
> * [Qwen3-VL-8B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking)

> **32B Models**
>
> * [Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)
> * [Qwen3-VL-32B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking)

> **30B-A3B MoE**
>
> * [Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)
> * [Qwen3-VL-30B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking)

> **235B-A22B MoE**
>
> * [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct)
> * [Qwen3-VL-235B-A22B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking)

## Model Selection Guide

### By Use Case

**Edge/Mobile Applications**

* Choose: **2B or 4B Instruct**
* Rationale: Low memory footprint, fast inference

**General Purpose Vision-Language Tasks**

* Choose: **8B Instruct**
* Rationale: Best balance of performance and efficiency

**Complex Reasoning Tasks**

* Choose: **8B or 32B Thinking**
* Rationale: Enhanced reasoning capabilities

**Maximum Performance**

* Choose: **235B-A22B Instruct/Thinking**
* Rationale: State-of-the-art results across benchmarks

**Production with Budget Constraints**

* Choose: **30B-A3B Instruct**
* Rationale: MoE efficiency with strong performance

### By Hardware

```python theme={null}
# Single Consumer GPU (8-16GB)
model = "Qwen3-VL-2B-Instruct"

# Single High-End GPU (24GB+)
model = "Qwen3-VL-8B-Instruct"

# Multi-GPU (A100/H100)
model = "Qwen3-VL-32B-Instruct"

# H100+ with FP8 Support
model = "Qwen3-VL-235B-A22B-Instruct-FP8"
```

## Quantized Versions

For memory-constrained deployments, FP8 quantized versions are available:

* Requires NVIDIA H100+ and CUDA 12+
* Minimal performance degradation
* Significant memory savings
* Faster inference throughput

**Available**: All models have FP8 variants in the [HuggingFace collection](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe)

## Performance Benchmarks

For detailed performance comparisons across different model sizes and editions, see the [Benchmarks](https://mintlify.com/QwenLM/Qwen3-VL/resources/benchmarks.md) page.

> [!WARNING]
>
> MoE models require specialized deployment configuration. Ensure your inference framework supports expert parallelism (e.g., vLLM with `--enable-expert-parallel` flag).


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