Qwen 3 Omni 30B-A3B
By Alibaba · China
Updated 2026-07-13
Overview
Alibaba's omni-modal 30B MoE (3B active) with streaming speech, 119-language ASR, and Apache 2.0 licensing. The most accessible truly omnimodal open model.
When to pick this model
- Voice-first assistants with low-latency speech in/out
- Multilingual ASR across 119 languages
- Real-time multimodal agents on a single GPU
- Long-context multimodal reasoning (131k)
- Apache 2.0 commercial deployments
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 62 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Qwen 3 Omni 30B-A3B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 62 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwen 3 Omni 30B-A3B needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwen 3 Omni 30B-A3B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwen 3 Omni 30B-A3B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run Qwen 3 Omni 30B-A3B?
To run Qwen 3 Omni 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Strengths
- Native omnimodal I/O: text, image, audio in and out
- 131k context
- Streaming speech for low-latency voice apps
- Apache 2.0 license
- Only 3B active params per token
Limitations
- Around 19 GB VRAM in Q4
- Audio path is still maturing relative to text and vision
- Tooling support uneven outside vLLM
Typical workloads
In our catalog grid, Qwen 3 Omni 30B-A3B is filed under Omni MoE, Multilingual Speech — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; audio understanding.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE · 30B · Qwen3-Omni · text + vision + audio end-to-end
Training: Qwen3-Omni 30B — Qwen omnimodal model (text, images, audio in/out).
The default open choice if you actually need audio in and out, not just text and images.
Quick start
ollama run qwen3-omni:30bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwen 3 Omni 30B-A3B need?
At the recommended Q4_K_M quantization, Qwen 3 Omni 30B-A3B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 62 GB.
Can Qwen 3 Omni 30B-A3B run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwen 3 Omni 30B-A3B support?
Qwen 3 Omni 30B-A3B supports a 128k-token context window (131,072 tokens).
Can I use Qwen 3 Omni 30B-A3B commercially?
Yes. Qwen 3 Omni 30B-A3B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Qwen 3 Omni 30B-A3B on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwen 3 Omni 30B-A3B should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.