Qwen2.5 Omni 7B — Hardware Requirements & GPU Compatibility
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Qwen2.5-Omni-7B is Alibaba's 10.7-billion-parameter flagship in the Qwen2.5-Omni family, an end-to-end model that perceives text, images, audio, and video and generates both text and natural streaming speech in response. It shares the family's Thinker-Talker design and TMRoPE positional scheme for synchronizing audio and video timestamps, tuned for low-latency, real-time conversation rather than turn-based chat alone. At just under 11 billion parameters, it needs a single mainstream-to-high-end consumer GPU once quantized. Its language backbone carries a 32,768 token context window. It is released under Alibaba's Qwen Research License, a non-commercial license limited to research and evaluation rather than the Apache 2.0 used for Qwen's text-only models. Published in March 2025, it was the first Omni release, with the smaller 3B variant following about a month later.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~93 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~20 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2.5 Omni 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 14.1 GB free · 16K context
Download: Qwen2.5-Omni-7B-Q4_K_M.gguf4.68 GBggml-org
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (2 GB headroom) · Q4_K_M
- Generation speed
- ~84 tok/s
- generation speed
- Cost per 1M output tokens
- $0.41
- per 1M output tokens
How Much VRAM Does Qwen2.5 Omni 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 5.0 GB | 11 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~131 tok/s | |
| 5.2 GB | 11.1 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~127 tok/s | |
| 5.8 GB | 11.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~114 tok/s | |
| 5.9 GB | 11.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~111 tok/s | |
| 7.1 GB | 13.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~93 tok/s | |
| 8.4 GB | 14.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~78 tok/s | |
| 9.7 GB | 15.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~67 tok/s | |
| 11.8 GB | 17.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~56 tok/s |
Selected Q4_K_M
4-bit medium quantization9.9GBat 16K context
- Weights
- 6.4 GB
- KV cache
- 3.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 14.1 GB free · ~93 tok/s
≥50 tok/s needs ≥544 GB/s · RTX 5090 ~165, RTX 3090 Ti ~93
Which Hardware Can Run Qwen2.5 Omni 7B?
Q4_K_M · 7.1 GBQwen2.5 Omni 7B (Q4_K_M) requires 7.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080. 151 devices with unified memory can run Qwen2.5 Omni 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
12 GB
12 GB tier →fits · 2.1 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~84Simulate output on RTX 3080 TiRTX 5070~62Simulate output on RTX 5070RTX 4070~46Simulate output on RTX 407020 GB
fits · 10.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~68Simulate output on RX 7900 XT32–40 GB
fits · 22.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~165Simulate output on RTX 5090M5 Max36 GB~46Simulate output on Mac Studio M5 Max (36 GB)M532 GB~15Simulate output on MacBook Air 13" M5 (32 GB)Orin~19Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~12Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen2.5 Omni 7B
Qwen 2.5 family · 44 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| GLM 4.1V 9B ThinkingZ.ai | — | 7.2 GB | ~100 | same size |
| INTELLECT 1 InstructPrimeIntellect | — | 7.8 GB | ~97 | same size |
| Llama 3.2 11B Vision InstructMeta | — | 9.8 GB | ~93 | same size |
| Falcon 11BTII UAE | — | 8.5 GB | ~89 | same size |
| GigaChat3.1 Audio 10B A1.8Bai-sage | — | 22.9 GB | ~198 | same size |
| Bielik 11B V3.0 Instructspeakleash | — | 27.4 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 10.7B
- Architecture
- Qwen2_5OmniModel
- Release Date
- 2025-03-22
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen2.5 Omni 7B need?
Qwen2.5 Omni 7B requires 7.1 GB of VRAM at Q4_K_M, or 23.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 10.7B × 4.8 bits ÷ 8 = 6.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Qwen2.5 Omni 7B?
For Qwen2.5 Omni 7B, Q4_K_M (7.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (8.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 Omni 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen2.5 Omni 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 7.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen2.5 Omni 7B locally?
Yes — Qwen2.5 Omni 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2.5 Omni 7B?
At Q4_K_M, Qwen2.5 Omni 7B can reach ~678 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~93 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 7.0800 × 0.65 = ~734 tok/s
Estimated speed at Q4_K_M (7.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen2.5 Omni 7B?
At Q4_K_M, the download is about 6.44 GB. The full-precision BF16 version is 21.46 GB. The smallest option (IQ2_XXS) is 2.95 GB.
Which GPUs can run Qwen2.5 Omni 7B?
52 consumer GPUs can run Qwen2.5 Omni 7B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 38 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen2.5 Omni 7B?
151 devices with unified memory can run Qwen2.5 Omni 7B at Q4_K_M (7.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.