Qwen3 Omni 30B A3B Thinking — Hardware Requirements & GPU Compatibility
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Qwen3-Omni-30B-A3B-Thinking is the reasoning-focused variant of Alibaba's Qwen3-Omni family, a mixture-of-experts model that accepts text, audio, image, and video input and reasons over it with explicit chain-of-thought before answering in text. Unlike the Instruct variant, it keeps only the "thinker" component and drops the speech-generating "talker" and audio codec, so it outputs text only, not speech. Its backbone still routes across 128 experts with 8 active per token, roughly 3 billion active out of about 31.7 billion total, and needs a high-end consumer GPU or multi-GPU setup once quantized. Context length is 65,536 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2025, a few days ahead of the Instruct variant that adds real-time speech generation on top of the same reasoning core.
Runs great on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~139 tok/s · 8K)Macs from 24 GB (Q2_K · ~51 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 Omni 30B A3B Thinking
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/Qwen3-Omni-30B-A3B-Thinking-GGUF:Q4_K_MQ4_K_M · needs ~24.7 GB · 16K context
Download: Qwen3-Omni-30B-A3B-Thinking-Q4_K_M.gguf18.56 GBggml-org
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 32 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
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Runs on RTX PRO 4500 (32 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~129 tok/s
- generation speed
- Cost per 1M output tokens
- $0.73
- per 1M output tokens
How Much VRAM Does Qwen3 Omni 30B A3B Thinking Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.8 GB | 22.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~170 tok/s | |
| 17.0 GB | 25.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~157 tok/s | |
| 20.9 GB | 29 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~139 tok/s | |
| 24.9 GB | 33 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 28.6 GB | |
| 28.8 GB | 36.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 32.6 GB | |
| 34.9 GB | 43 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 38.7 GB | |
| 69.8 GB | 77.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 73.6 GB |
Selected Q4_K_M
4-bit medium quantization24.7GBat 16K context
- Weights
- 20.1 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Which Hardware Can Run Qwen3 Omni 30B A3B Thinking?
Q4_K_M · 20.9 GBQwen3 Omni 30B A3B Thinking (Q4_K_M) requires 20.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 100 devices with unified memory can run Qwen3 Omni 30B A3B Thinking, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit Q2_K (18.6 GB)
1 GPU
RX 7900 XT~71Simulate output on RX 7900 XT32–40 GB
fits · 7.3 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~192Simulate output on RTX 5090M5 Max36 GB~53Simulate output on Mac Studio M5 Max (36 GB)M532 GB~27Simulate output on MacBook Air 13" M5 (32 GB)Orin~40Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~26Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3 Omni 30B A3B Thinking
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Nanbeige4.3 33BNanbeige | — | 23.7 GB | ~32 | same size |
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Granite Switch 4.1 30BIBM | — | 22.7 GB | ~35 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 31.7B
- Active parameters
- 4.5B per token
- Architecture
- Qwen3OmniMoeForConditionalGeneration
- Release Date
- 2025-09-15
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen3 Omni 30B A3B Thinking need?
Qwen3 Omni 30B A3B Thinking requires 20.9 GB of VRAM at Q4_K_M, or 69.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 31.7B × 4.8 bits ÷ 8 = 19 GB
KV Cache + Overhead ≈ 1.9 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
Can NVIDIA GeForce RTX 4090 run Qwen3 Omni 30B A3B Thinking?
Yes, at Q4_K_M (20.9 GB) or lower. Higher quantizations like Q5_K_M (24.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3 Omni 30B A3B Thinking?
For Qwen3 Omni 30B A3B Thinking, Q4_K_M (20.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 Omni 30B A3B Thinking on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Qwen3 Omni 30B A3B Thinking, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.9 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).
Can I run Qwen3 Omni 30B A3B Thinking locally?
Yes — Qwen3 Omni 30B A3B Thinking can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 Omni 30B A3B Thinking?
At Q4_K_M, Qwen3 Omni 30B A3B Thinking can reach ~98 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~139 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 3.0 GB active ÷ (8000 × 0.65) = 0.58 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 3.22 ms = ~311 tok/s
Estimated speed at Q4_K_M (20.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 Omni 30B A3B Thinking?
At Q4_K_M, the download is about 19.03 GB. The full-precision BF16 version is 63.44 GB. The smallest option (Q2_K) is 13.48 GB.
Which GPUs can run Qwen3 Omni 30B A3B Thinking?
7 consumer GPUs can run Qwen3 Omni 30B A3B Thinking at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Qwen3 Omni 30B A3B Thinking?
100 devices with unified memory can run Qwen3 Omni 30B A3B Thinking at Q4_K_M (20.9 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.