This qwen3vl model was trained 2x faster with Unsloth and Huggingface's TRL library.
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
Parameters2.1B
Context262,144
Weights4.3 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads3.1M
Runs On
What it takes to serve Qwen3-VL-2B-Instruct (2.1B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 4.3 GB | 5.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 2.1 GB | 2.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 1.1 GB | 1.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By Qwen, published under apache-2.0, revision 89644892e4d8.
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
Key Enhancements:
Configuration
- Architecture
- Qwen3VLForConditionalGeneration
- Context length (tokens)
- 262,144
- Layers
- 28
- Hidden size
- 2,048
- Feed-forward size
- 6,144
- Attention heads
- 16
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 151,936
- RoPE base
- 5,000,000
- Model type
- qwen3_vl
Identity and Version
- Repository
- Qwen/Qwen3-VL-2B-Instruct
- Publisher
- Qwen
- Task
- Image and text to text
- Modality
- Image and text
- Library
- transformers
- Parameters
- 2.1B parameters
- Languages
- Not stated by the source
- Revision
- 89644892e4d85e24eaac8bacfd4f463576704203
- First published
- 2025-10-19
- Last updated
- 2025-10-23
Files and Weights
12 files, 4.3 GB in total. The weights are 1 file totalling 4.3 GB in safetensors.
Weights1 file · 4.3 GB
Configuration5 files · 8.1 KB
Tokenizer4 files · 11.5 MB
Documentation1 file · 7.1 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 4.3 GB | 7de1838c87a5 |
| chat_template.json | Configuration | 5.5 KB | — |
| config.json | Configuration | 1.5 KB | — |
| generation_config.json | Configuration | 269 B | — |
| preprocessor_config.json | Configuration | 390 B | — |
| video_preprocessor_config.json | Configuration | 385 B | — |
| README.md | Documentation | 7.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 7.0 MB | — |
| tokenizer_config.json | Tokenizer | 10.9 KB | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 4.3 GB
Download from Qwen
Released by Qwen through ModelScope. Read the license.
Built From
- Described by arXiv:2308.12966
- Described by arXiv:2409.12191
- Described by arXiv:2502.13923
- Described by arXiv:2505.09388
Evaluations
Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| tiiuae/PBench | Task averageMetric averageSetup Detection-only model combined with SAM2 to convert boxes to segmentation masks.Comparison conditions not established | 37 | Community Evals Reported by a third party |
Evaluated revision not stated | 2026-05-11 |
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 4.3 GB |
| 16-bit | 4.3 GB |
| 8-bit | 2.1 GB |
| 4-bit | 1.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Derived fromQwen3-VL-Embedding-2B
- Derived fromQwen3-VL-Reranker-2B
- Derived fromCosmos-Reason2-2B
- Derived fromProcVLM-2B
Compare Qwen3-VL-2B-Instruct
Questions About Qwen3-VL-2B-Instruct
How much GPU memory does Qwen3-VL-2B-Instruct need?
About 5.1 GB at 16-bit and 1.3 GB at 4-bit: the weights (2.1B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Qwen3-VL-2B-Instruct on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Qwen3-VL-2B-Instruct commercially?
Yes. Qwen3-VL-2B-Instruct is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is Qwen3-VL-2B-Instruct's context length?
262,144 tokens, from the maximum position embeddings in its published configuration.
Similar Models
We're excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation. SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on…
Model · Image and text to text
Qari-OCR-v0.3-VL-2B-Instruct
QARI-OCR v0.3 is a specialized vision-language model fine-tuned for Arabic Optical Character Recognition with a focus on structural document understanding. - Built on Qwen2-VL-2B-Instruct, this model excels at preserving document layouts, HTML tags, and formatting while transcribing Arabic text. - It is described in detail in the paper QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation. While QARI v0.2 achieves better raw text accuracy (CER: 0.061), QARI v0.3 excels in: - HTML/Markdown structure preservation - Document layout understanding - Handwritten text recognition (initial capabilities) - 5x faster training than v0.2 You can load this…
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not applicable. Scores of Qwen3.5 models are reported…
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Rax 4.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. Rax 4.5 features the following enhancement: For more details, please refer to our blog post Rax 4.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not…
This is Qwen3.5-2B auto-optimized by Claude Fable for fast single-request text generation. Fable built and tuned the included qwen35fast inference engine while keeping Qwen's original BF16 weights unchanged. Across 12 development workloads, the Fable engine delivered 14× the decode speed of Transformers eager and 1.02× the speed of vLLM with MTP (geometric means). On 12 held-out workloads, it reached 528–866 tokens/s and 1.01× vLLM with MTP. Use Python 3.12 and an NVIDIA CUDA GPU. Download the model and install its dependencies: The original checkpoint also works with Transformers for Qwen's standard text and vision-language workflows; the speed figures above use qwen35fast. - Captured the…