Family Qwen · 7B parameters

Qwen 2.5 Omni 7B

First open omni model: text+image+audio+video input, text+speech output. No official Ollama tag.

🇨🇳 Alibaba·License Apache 2.0·Context 32k tokens·Output March 2025·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • Text + image + audio + video input
  • Speech output
  • Apache 2.0
Limitations to know
  • —No official Ollama tag
  • —Community GGUF only
Architecture
Thinker-Talker end-to-end · TMRoPE · streaming speech in+out
Training
First open consumer omni model.
Ideal for
Omni assistantReal-time voice

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.

$# GGUF : ggml-org/Qwen2.5-Omni-7B-GGUF (pas d'Ollama officiel)
⚠
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
6 GB
Q5_K_M
Good quality/size compromise
7 GB
Q8_0
Nearly indistinguishable from FP16
10 GB
FP16
Full precision — server use
18 GB
Fallback CPU · If you don't have a GPU, allow 12 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Qwen 2.5 Omni 7B?

To run Qwen 2.5 Omni 7B locally with Q4 quantization, you need about 6 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: RTX 5060 Ti 16GB (ASUS Prime)
AmazonSee price →

Affiliate links — possible commission at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: Qwen 2.5 Omni 7B also runs on a RTX laptop PC (16 GB of VRAM) →

This model in your private ChatGPT, without the cloud

Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.

  • Lifetime online access
  • PDF + files
  • Lifetime updates

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
~8t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~25t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~60t/s
RTX 4090, M4 Max, Radeon 7900

04Public benchmarks

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

OmniBench (avg)
56.13