Skip to content
localmodel.run

Text model · Qwen2.5-VL

Qwen2.5-VL 7B: RAM and VRAM requirements

Qwen2.5-VL 7B needs about 7.1 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~5.62 GB to download; KV cache and overhead add the rest), or about 11.1 GB at Q8_0. The lightest hardware that runs it is Nvidia GeForce RTX 3060 (12GB).

Qwen2.5-VL family · 8.29B params · released Jan 2025.

Shopping for hardware? See what runs Qwen2.5-VL 7B →

Vision · accepts images License Apache-2.0 · Commercial OK ↓ 9.3M/mo ♥ 1.7K on HuggingFace
Q4_K_M GGUF
5.62 GB
Q8_0 GGUF
9.6 GB
Memory @ Q4 (4k)
~7.1 GB
Context
32 k

Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.

Will it run on your device?

Qwen2.5-VL 7B runs on 33 of 40 tracked devices at Q4_K_M.

31 run well 2 tight fit 7 too small
Fit check Q4_K_M
Yes, it runs fast
needs 7.1 GB usable 10.5 GB
See the full breakdown

Memory breakdown

Weights (Q4_K_M)5.62 GB
+
KV cache (4k)0.7 GB
+
Overhead0.8 GB
=
Total7.1 GB

How context length changes it

4k context ~7.1 GB 32k context ~11.9 GB 128k context ~28.5 GB

Longer context grows the KV cache quickly: Qwen2.5-VL 7B needs ~7.1 GB at 4k but ~28.5 GB at 128k, which can push it past a device that fits it at a short context.

Speed drops too: every token re-reads the KV cache, so at 128k context Qwen2.5-VL 7B generates at roughly ~20% of its short-context speed. How this is estimated.

Quantization sizes

GGUF quantson disk
Quantization Size on disk
Q2_K 3.5 GB est
Q3_K_M 4.1 GB est
Q4_K_M (default) 5.62 GB
Q5_K_M 5.9 GB est
Q6_K 6.8 GB est
Q8_0 9.6 GB
FP16 16.6 GB est

Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.

Run it

llama.cpp
$ llama-cli -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
LM Studio
$ lms get ggml-org/Qwen2.5-VL-7B-Instruct-GGUF

Which devices can run Qwen2.5-VL 7B?

Apple Silicon Macs

RAM-only laptops

iPhone & iPad

Android

NVIDIA GPUs

AMD GPUs

Same job, different size

Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.

Next size down DeepSeek-R1-0528-Qwen3-8B 8.19B Next size up LFM2.5 8B-A1B 8.3B

Similar models

Head-to-head

FAQ

How much VRAM or RAM does Qwen2.5-VL 7B need?

At Q4_K_M, Qwen2.5-VL 7B needs about 7.1 GB (weights ~5.62 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~11.1 GB.

What is the Q4_K_M GGUF file size of Qwen2.5-VL 7B?

The Q4_K_M GGUF file is about 5.62 GB to download, and the Q8_0 GGUF is about 9.6 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~7.1 GB of memory at Q4_K_M.

Can Qwen2.5-VL 7B run on a laptop?

Qwen2.5-VL 7B is large; you need a 24 GB+ GPU or a 32-48 GB Mac at Q4_K_M.

Can I use Qwen2.5-VL 7B commercially?

Yes. Qwen2.5-VL 7B is licensed Apache-2.0, which permits commercial use.

Understand the numbers

Short guides to the ideas behind Qwen2.5-VL 7B's memory and quant figures.

Qwen2.5-VL 7B vision-language model: chats about images. Q4_K_M ~4.4GB plus a ~1.26GB fp16 vision projector (mmproj), folded into the totals. Sizes from the ggml-org GGUF repo. Vision needs an mmproj-capable runtime; many mobile runtimes do not support it yet.

Sources

Last validated 2026-08-03. Memory figures are estimates. See methodology.