Qwen3 Omni 30B A3B Instruct — Hardware Requirements & GPU Compatibility
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Qwen3-Omni-30B-A3B-Instruct is Alibaba's flagship omni-modal model, a mixture-of-experts Thinker-Talker system that processes text, images, audio, and video and responds with real-time streaming text and speech. Its text backbone routes across 128 experts with 8 active per token, roughly 3 billion active parameters out of around 35 billion total, since it keeps both the "thinker" and the speech-generating "talker" plus its audio codec resident in memory. It supports 119 text languages, 19 spoken input languages, and 10 spoken output languages, and needs a high-end consumer GPU or multi-GPU setup once quantized. Its language backbone supports a 65,536 token context window. It is released under the Apache 2.0 license, and was published in September 2025 as the instruct counterpart to Qwen3-Omni-30B-A3B-Thinking, which drops the talker to focus on text output with chain-of-thought reasoning.
655.6K downloads
1.0K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 35.3B
- Architecture
- Qwen3OmniMoeForConditionalGeneration
- Release Date
- 2025-09-20
- License
- Other
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HuggingFace
How Much VRAM Does Qwen3 Omni 30B A3B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 16.5 GB | — | 14.99 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 18.9 GB | — | 17.19 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 19.4 GB | — | 17.63 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 23.3 GB | — | 21.16 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 27.6 GB | — | 25.12 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 32 GB | — | 29.09 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 38.8 GB | — | 35.26 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 77.6 GB | — | 70.52 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 Instruct?
Q4_K_M · 23.3 GBQwen3 Omni 30B A3B Instruct (Q4_K_M) requires 23.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 31+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Which Devices Can Run Qwen3 Omni 30B A3B Instruct?
Q4_K_M · 23.3 GB41 devices with unified memory can run Qwen3 Omni 30B A3B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson AGX Orin 32GB.
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3 Omni 30B A3B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Qwen3 Omni 30B A3B Instruct need?
Qwen3 Omni 30B A3B Instruct requires 23.3 GB of VRAM at Q4_K_M, or 77.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 35.3B × 4.8 bits ÷ 8 = 21.2 GB
KV Cache + Overhead ≈ 2.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M23.3 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 Omni 30B A3B Instruct?
Yes, at Q4_K_M (23.3 GB) or lower. Higher quantizations like Q5_K_M (27.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 Omni 30B A3B Instruct?
For Qwen3 Omni 30B A3B Instruct, Q4_K_M (23.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (27.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 16.5 GB.
VRAM requirement by quantization
Q2_K16.5 GBQ4_019.4 GBQ4_K_M ★23.3 GBQ5_K_M27.6 GBQ6_K32.0 GBBF1677.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 Omni 30B A3B Instruct on a Mac?
Qwen3 Omni 30B A3B Instruct requires at least 16.5 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 Instruct locally?
Yes — Qwen3 Omni 30B A3B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 23.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 Omni 30B A3B Instruct?
At Q4_K_M, Qwen3 Omni 30B A3B Instruct 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 ÷ 23.3 × 0.65 = ~331 tok/s
Estimated speed at Q4_K_M (23.3 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 Instruct?
At Q4_K_M, the download is about 21.16 GB. The full-precision BF16 version is 70.52 GB. The smallest option (Q2_K) is 14.99 GB.
- Which GPUs can run Qwen3 Omni 30B A3B Instruct?
7 consumer GPUs can run Qwen3 Omni 30B A3B Instruct at Q4_K_M (23.3 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
- Which devices can run Qwen3 Omni 30B A3B Instruct?
41 devices with unified memory can run Qwen3 Omni 30B A3B Instruct at Q4_K_M (23.3 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.