What are the hardware requirements for Qwen2 VL 7B Instruct GPTQ Int8?

Qwen2 VL 7B Instruct GPTQ Int8 requires approximately 10.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2 VL 7B Instruct GPTQ Int8 and how large is it?

Qwen2 VL 7B Instruct GPTQ Int8 is developed by Qwen, a model with 7b 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 Qwen2 VL 7B Instruct GPTQ Int8?

Qwen2 VL 7B Instruct GPTQ Int8 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2 VL 7B Instruct GPTQ Int8 by Qwen — VRAM 10.2GB, 32K context

Name: Qwen2 VL 7B Instruct GPTQ Int8
Author: Qwen

Qwen2 VL 7B Instruct GPTQ Int8 is an open-source language model by Qwen. Features: 7b LLM, VRAM: 10.2GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.15.

Arxiv:2308.12966 Arxiv:2409.12191 8-bit Base model:quantized:qwen/qwen... Base model:qwen/qwen2-vl-7b-in... Conversational En Gptq Image-text-to-text Instruct Multimodal Quantized Qwen2 vl Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8

Qwen2 VL 7B Instruct GPTQ Int8 Parameters and Internals

Model Type

multimodal, text-generation

Use Cases

Areas:

research, commercial applications

Applications:

visual understanding, video-based question answering, dialog, content creation

Primary Use Cases:

complex reasoning and decision making when integrated with devices

Limitations:

Lack of Audio Support, Data timeliness, Constraints in Individuals and IP, Limited Capacity for Complex Instruction, Insufficient Counting Accuracy, Weak Spatial Reasoning Skills

Supported Languages

en (English), zh (Chinese), multi (most European languages, Japanese, Korean, Arabic, Vietnamese)

Input Output

Accepted Modalities:

text, image, video

Output Format:

Generated response

Performance Tips:

Enable flash_attention_2 for better acceleration and memory saving.

LLM Name	Qwen2 VL 7B Instruct GPTQ Int8
Repository 🤗	https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8
Base Model(s)	Qwen2 VL 7B Instruct Qwen/Qwen2-VL-7B-Instruct
Model Size	7b
Required VRAM	10.2 GB
Updated	2026-07-31
Maintainer	Qwen
Model Type	qwen2_vl
Instruction-Based	Yes
Model Files	4.0 GB: 1-of-3 4.0 GB: 2-of-3 2.2 GB: 3-of-3
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	Qwen2VLForConditionalGeneration
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.45.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	float16
Errors	replace

Rank the Qwen2 VL 7B Instruct GPTQ Int8 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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 Qwen2 VL 7B Instruct GPTQ Int8

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2 VL 7B Instruct GPTQ Int4	32K / 7 GB	2588	14
...L 7B Instruct Unsloth Bnb 4bit	32K / 6.8 GB	5860	13
OlmOCR 7B 0225 Preview 4bit	32K / 5.6 GB	22	1
...L 7B Instruct Abliterated 8bit	32K / 8.9 GB	29	1
Qwen2 VL 7B Instruct Bnb 4bit	32K / 5.9 GB	936	6
Qwen2 VL 7B Instruct 4bit	32K / 4.7 GB	648	2
Qwen2 VL 7B Instruct 8bit	32K / 8.9 GB	36	2
OlmOCR 7B 0225 Preview	32K / 16.6 GB	18655	706
Qwen2 VL 7B Instruct	32K / 16.7 GB	33	37
OlmOCR 7B 0225 Preview FP8	32K / 10 GB	127	9

Note: green Score (e.g. "73.2") means that the model is better than Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8.