What are the hardware requirements for Qwen2.5 VL 7B Surg CholecT50?

Qwen2.5 VL 7B Surg CholecT50 requires approximately 16.6 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 Surg CholecT50 and how large is it?

Qwen2.5 VL 7B Surg CholecT50 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 Surg CholecT50?

Qwen2.5 VL 7B Surg CholecT50 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 Surg CholecT50 by nvidia — VRAM 16.6GB, 125K context

Name: Qwen2.5 VL 7B Surg CholecT50
Author: nvidia

Qwen2.5 VL 7B Surg CholecT50 is an open-source language model by nvidia. Features: 7b LLM, VRAM: 16.6GB, Context: 125K, License: other, LLM Explorer Score: 0.22.

Arxiv:2204.05235 Conversational Dataset:camma-public/cholect50 En Endpoints compatible Healthcare Image-text-to-text Model-index Multimodal-llm Qwen2 5 vl Region:us Safetensors Sharded Surgical Tensorflow

Model Card on HF 🤗: https://huggingface.co/nvidia/Qwen2.5-VL-7B-Surg-CholecT50

Qwen2.5 VL 7B Surg CholecT50 Parameters and Internals

LLM Name	Qwen2.5 VL 7B Surg CholecT50
Repository 🤗	https://huggingface.co/nvidia/Qwen2.5-VL-7B-Surg-CholecT50
Base Model(s)	Qwen/Qwen2.5-VL-7B Qwen/Qwen2.5-VL-7B
Model Size	7b
Required VRAM	16.6 GB
Updated	2026-08-11
Maintainer	nvidia
Model Type	qwen2_5_vl
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.7 GB: 4-of-4 0.0 GB 0.0 GB
Supported Languages	en
Model Architecture	Qwen2_5_VLForConditionalGeneration
License	other
Context Length	128000
Model Max Length	128000
Transformers Version	4.50.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 VL 7B Surg CholecT50 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 Surg CholecT50

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
UI TARS 1.5 7B	125K / 33.2 GB	887611	587
RolmOCR	125K / 16.6 GB	224280	589
Fara 7B	125K / 16.6 GB	2059	619
Qwen2.5 VL 7B Instruct FP4	125K / 7.2 GB	49165	3
PosterCopilot	125K / 16.7 GB	29	1
OlmOCR 7B 0825 FP8	125K / 10 GB	38011	10

Note: green Score (e.g. "73.2") means that the model is better than nvidia/Qwen2.5-VL-7B-Surg-CholecT50.