Qwen3 Omni 30B A3B Thinking — Hardware Requirements & GPU Compatibility
Chat
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.
326.5K downloads
323 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 31.7B
- Architecture
- Qwen3OmniMoeForConditionalGeneration
- Release Date
- 2025-09-15
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Qwen3 Omni 30B A3B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 14.8 GB | — | 13.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 17.0 GB | — | 15.46 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 20.9 GB | — | 19.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 24.9 GB | — | 22.60 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 28.8 GB | — | 26.17 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 34.9 GB | — | 31.72 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 69.8 GB | — | 63.44 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs 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.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3 Omni 30B A3B Thinking?
Q4_K_M · 20.9 GB41 devices with unified memory can run Qwen3 Omni 30B A3B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3 Omni 30B A3B Thinking
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
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)
VRAM usage by quantization
Q4_K_M20.9 GB- 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
Q2_K14.8 GBQ4_K_M ★20.9 GBQ5_K_M24.9 GBQ6_K28.8 GBQ8_034.9 GBBF1669.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 Omni 30B A3B Thinking on a Mac?
Qwen3 Omni 30B A3B Thinking requires at least 14.8 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3 Omni 30B A3B Thinking locally?
Yes — Qwen3 Omni 30B A3B Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 20.9 GB of VRAM. 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 ~100 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~177 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 ÷ 20.9 × 0.65 = ~331 tok/s
Estimated speed at Q4_K_M (20.9 GB)
~331 tok/s~177 tok/s~331 tok/s~307 tok/sReal-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 (20.9 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 Omni 30B A3B Thinking?
41 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, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.