Qwen2.5 Omni 3B — Hardware Requirements & GPU Compatibility
Chat
Qwen2.5-Omni-3B is Alibaba's 5.5-billion-parameter end-to-end omni-modal model, built to perceive text, images, audio, and video and respond with both text and natural streaming speech. It uses a Thinker-Talker architecture with a time-aligned position embedding (TMRoPE) that synchronizes video frames with audio, enabling real-time voice and video chat with chunked input and immediate output. As the smaller of the two Omni checkpoints, it runs on a single consumer GPU once quantized. Its language backbone supports a 32,768 token context window. It is released under Alibaba's Qwen Research License, which permits only non-commercial research and evaluation, unlike the Apache 2.0 used for Qwen's text-only models. Published in April 2025, about a month after the larger Qwen2.5-Omni-7B, it brings real-time multimodal chat to smaller-scale deployments.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~180 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~20 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2.5 Omni 3B
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-3B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 18.0 GB free · 16K context
Download: Qwen2.5-Omni-3B-Q4_K_M.gguf2.10 GBggml-org
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (6 GB headroom) · Q4_K_M
- Generation speed
- ~64 tok/s
- generation speed
- Cost per 1M output tokens
- $0.26
- per 1M output tokens
How Much VRAM Does Qwen2.5 Omni 3B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.6 GB | 7.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~253 tok/s | |
| 2.7 GB | 7.6 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~246 tok/s | |
| 3.0 GB | 7.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~221 tok/s | |
| 3.0 GB | 8 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~215 tok/s | |
| 3.6 GB | 8.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~180 tok/s | |
| 4.3 GB | 9.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~151 tok/s | |
| 5.0 GB | 10 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~131 tok/s | |
| 6.1 GB | 11.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~108 tok/s |
Selected Q4_K_M
4-bit medium quantization6.0GBat 16K context
- Weights
- 3.0 GB
- KV cache
- 2.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.0 GB free · ~180 tok/s
≥50 tok/s needs ≥280 GB/s · RTX 5090 ~319, RTX 3090 Ti ~180
Which Hardware Can Run Qwen2.5 Omni 3B?
Q4_K_M · 3.6 GBQwen2.5 Omni 3B (Q4_K_M) requires 3.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen2.5 Omni 3B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 2.0 GB free · up to 16K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~135Simulate output on RTX 3080RTX 2080 Ti~110Simulate output on RTX 2080 TiRTX 3070 Ti~108Simulate output on RTX 3070 TiM3*8 GB~20Simulate output on iMac 24" M3 (8 GB)Orin Nano~18Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~13Simulate output on iPhone 1720 GB
fits · 14.0 GB free · up to 32K ctx
1 GPU
RX 7900 XT~132Simulate output on RX 7900 XT32–40 GB
fits · 26.0 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~319Simulate output on RTX 5090M5 Max36 GB~88Simulate output on Mac Studio M5 Max (36 GB)M532 GB~30Simulate output on MacBook Air 13" M5 (32 GB)Orin~37Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~24Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen2.5 Omni 3B
Qwen 2.5 family · 44 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Youtu Parsing OmniTencent | — | 12.7 GB | ~59 | same size |
| Gemma 3n E2B ITGoogle | — | 5.9 GB | ~183 | same size |
| TLive Omni 4BTaoLiveAIGC | — | 13.3 GB | ~54 | same size |
| T5gemma 2B 2B Ul2Google | — | 6 GB | ~178 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 5.5B
- Architecture
- Qwen2_5OmniModel
- Release Date
- 2025-04-30
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen2.5 Omni 3B need?
Qwen2.5 Omni 3B requires 3.6 GB of VRAM at Q4_K_M, or 12.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 5.5B × 4.8 bits ÷ 8 = 3.3 GB
KV Cache + Overhead ≈ 0.4 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 3B?
For Qwen2.5 Omni 3B, Q4_K_M (3.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (4.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 Omni 3B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen2.5 Omni 3B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen2.5 Omni 3B locally?
Yes — Qwen2.5 Omni 3B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2.5 Omni 3B?
At Q4_K_M, Qwen2.5 Omni 3B can reach ~1315 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~180 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 ÷ 3.65 × 0.65 = ~1425 tok/s
Estimated speed at Q4_K_M (3.6 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 3B?
At Q4_K_M, the download is about 3.32 GB. The full-precision BF16 version is 11.07 GB. The smallest option (IQ2_XXS) is 1.52 GB.
Which GPUs can run Qwen2.5 Omni 3B?
52 consumer GPUs can run Qwen2.5 Omni 3B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen2.5 Omni 3B?
163 devices with unified memory can run Qwen2.5 Omni 3B at Q4_K_M (3.6 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.