Qwen 2.5 VL 7B

Overview

A 7B vision-language model from Alibaba with state-of-the-art results in its class, scoring 95.7 on DocVQA. Handles hour-long video, bounding-box grounding, and multilingual OCR.

When to pick this model

• You need strong document understanding and OCR on a single consumer GPU
• You're building pipelines around long video analysis or screenshot Q&A
• You need bounding-box grounding or structured JSON output from images
• You want commercial-friendly Apache licensing for a VLM

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	6 GB
Q5_K_M	7 GB
Q8_0	10 GB
FP16 (no quantization)	18 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
DocVQA	95.7
ChartQA	87.3
OCRBench	86.4

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• State-of-the-art vision performance at the 7B tier
• Excellent multilingual OCR
• Long video input (over 1 hour)
• Apache 2.0

Limitations

• Requires a VLM-capable backend (Ollama 0.5+ or vLLM)
• Smaller than 72B sibling for the hardest visual reasoning

Architecture & training

Architecture: ViT + Qwen2.5 LLM · window attention · mRoPE · dynamic resolution

Training: Supports video >1h, bbox grounding, structured JSON output.

Quick start

ollama run qwen2.5vl:7b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.