Family Qwen · 72B parameters

Qwen 2.5 VL 72B

Frontier vision. MMMU 70.2, MMBench 88.6. Qwen license (non-Apache), commercial use under 100M MAU.

🇨🇳 Alibaba·License Qwen License·Context 125k tokens·Output January 2025·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • Frontier vision
  • MMMU 70.2
  • 128k ctx
Limitations to know
  • —Qwen license (non-Apache): MAU clause
  • —40+ GB of VRAM in Q4
Architecture
ViT + LLM · GQA · SwiGLU · RMSNorm
Training
72B backbone + vision encoder.
Ideal for
Advanced visionDocument analysis

05Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run qwen2.5vl:72b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
42 GB
Q5_K_M
Good quality/size compromise
50 GB
Q8_0
Nearly indistinguishable from FP16
78 GB
FP16
Full precision — server use
144 GB
Fallback CPU · If you don't have a GPU, allow 64 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Qwen 2.5 VL 72B?

To run Qwen 2.5 VL 72B locally with Q4 quantization, you need about 42 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.

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This model in your private ChatGPT, without the cloud

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~1t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~6t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~20t/s
RTX 4090, M4 Max, Radeon 7900

04Public benchmarks

Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.

MMMU
70.2
MathVista
74.8