What are the hardware requirements for Qwen2.5 VL 7B Instruct FP4?

Qwen2.5 VL 7B Instruct FP4 requires approximately 7.2 GB of VRAM and supports a context window of 125K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 VL 7B Instruct FP4 and how large is it?

Qwen2.5 VL 7B Instruct FP4 is developed by nvidia, 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 Qwen2.5 VL 7B Instruct FP4?

Qwen2.5 VL 7B Instruct FP4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 VL 7B Instruct FP4 by nvidia — VRAM 7.2GB, 125K context

Name: Qwen2.5 VL 7B Instruct FP4
Author: nvidia

Qwen2.5 VL 7B Instruct FP4 is an open-source language model by nvidia. Features: 7b LLM, VRAM: 7.2GB, Context: 125K, License: other, Instruction-Based, LLM Explorer Score: 0.26.

8-bit Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-vl-7b-... Conversational Fp4 Instruct Model optimizer Modelopt Nvidia Quantized Qwen2 5 vl Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/nvidia/Qwen2.5-VL-7B-Instruct-FP4

Qwen2.5 VL 7B Instruct FP4 Parameters and Internals

LLM Name	Qwen2.5 VL 7B Instruct FP4
Repository 🤗	https://huggingface.co/nvidia/Qwen2.5-VL-7B-Instruct-FP4
Base Model(s)	Qwen/Qwen2.5-VL-7B-Instruct Qwen/Qwen2.5-VL-7B-Instruct
Model Size	7b
Required VRAM	7.2 GB
Updated	2025-10-10
Maintainer	nvidia
Model Type	qwen2_5_vl
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 2.2 GB: 2-of-2
Model Architecture	Qwen2_5_VLForConditionalGeneration
License	other
Context Length	128000
Model Max Length	128000
Transformers Version	4.56.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 VL 7B Instruct FP4 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 Qwen2.5 VL 7B Instruct FP4

Best Alternatives	Context / RAM	Downloads	Likes
OmniLong Qwen2.5 VL 7B	512K / 16.6 GB	7	2
Qwen2.5 VL 7B Instruct	125K / 16.7 GB	8740905	1666
OlmOCR 2 7B 1025 FP8	125K / 10 GB	540426	253
OlmOCR 2 7B 1025	125K / 16.6 GB	244728	157
RolmOCR	125K / 16.6 GB	224280	589
Cosmos Reason1 7B	125K / 16.6 GB	62913	244
SpaSEViLA Cold Start 7B	125K / 16.6 GB	19	0
Zoom IQA 7B	125K / 16.6 GB	14	1
OlmOCR 7B 0825 FP8	125K / 10 GB	38011	10
Embodied Navigator 7B GRPO	125K / 17.3 GB	20	1

Note: green Score (e.g. "73.2") means that the model is better than nvidia/Qwen2.5-VL-7B-Instruct-FP4.