What are the hardware requirements for Qwen3 VL 32B Instruct AWQ?

Qwen3 VL 32B Instruct AWQ requires approximately 20.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3 VL 32B Instruct AWQ and how large is it?

Qwen3 VL 32B Instruct AWQ is developed by QuantTrio, a model with 32b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Qwen3 VL 32B Instruct AWQ?

Qwen3 VL 32B Instruct AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 VL 32B Instruct AWQ by QuantTrio — VRAM 20.7GB

Name: Qwen3 VL 32B Instruct AWQ
Author: QuantTrio

Qwen3 VL 32B Instruct AWQ is an open-source language model by QuantTrio. Features: 32b LLM, VRAM: 20.7GB, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.31.

Arxiv:2308.12966 Arxiv:2409.12191 Arxiv:2502.13923 Arxiv:2505.09388 4-bit Awq Base model:quantized:qwen/qwen... Base model:qwen/qwen3-vl-32b-i... Conversational Endpoints compatible Image-text-to-text Instruct Quantized Qwen3 vl Region:us Safetensors Sharded Tensorflow Vllm

Model Card on HF 🤗: https://huggingface.co/QuantTrio/Qwen3-VL-32B-Instruct-AWQ

Qwen3 VL 32B Instruct AWQ Parameters and Internals

LLM Name	Qwen3 VL 32B Instruct AWQ
Repository 🤗	https://huggingface.co/QuantTrio/Qwen3-VL-32B-Instruct-AWQ
Base Model(s)	Qwen3 VL 32B Instruct Qwen/Qwen3-VL-32B-Instruct
Model Size	32b
Required VRAM	20.7 GB
Updated	2026-08-09
Maintainer	QuantTrio
Model Type	qwen3_vl
Instruction-Based	Yes
Model Files	3.0 GB: 1-of-7 3.0 GB: 2-of-7 3.0 GB: 3-of-7 3.0 GB: 4-of-7 3.0 GB: 5-of-7 2.8 GB: 6-of-7 2.9 GB: 7-of-7
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Qwen3VLForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	4.57.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Torch Data Type	float16
Errors	replace

Rank the Qwen3 VL 32B Instruct AWQ capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Qwen3 VL 32B Instruct AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3 VL 32B Instruct Bnb 4bit	0K / 20.5 GB	298846	6
Qwen3 VL 32B Instruct 8bit	0K / 36.1 GB	2925	1
Qwen3 VL 32B Instruct 4bit	0K / 19.6 GB	388	1
... 32B Instruct Unsloth Bnb 4bit	0K / 32.5 GB	1445	2
Qwen3 VL 32B Instruct	0K / 67 GB	2881482	223
Qwen3 VL 32B Instruct FP8	0K / 35.7 GB	380619	47
Qwen3 VL 32B Instruct FP8	0K / 35.7 GB	30	1
Cosmos Reason2 32B	0K / 66.4 GB	2662	14
Qwen3 VL 32B Instruct	0K / 67 GB	330	5
...truct Ultra Uncensored Heretic	0K / 66.7 GB	310	11

Note: green Score (e.g. "73.2") means that the model is better than QuantTrio/Qwen3-VL-32B-Instruct-AWQ.