AlibabaQwen 3Qwen3OmniMoeForConditionalGeneration

Qwen3 Omni 30B A3B Instruct — Hardware Requirements & GPU Compatibility

Chat

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.

35.3B params · MoE3B activeOtherSep 2025478.6K downloads1.1K likes237.4K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Decent on a 24 GB GPU

Runs on 24 GB (Q4_K_M · ~177 tok/s · 4K)Macs from 24 GB (Q2_K · ~62 tok/s)

Other

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Qwen3 Omni 30B A3B Instruct

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.

Ways to runOther ways

each opens step-by-step

Run 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
Chat right here or connect any app

$5+ bonus for new users

Run Qwen3 Omni 30B A3B Instruct

27.2 GB VRAM at 16K context

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --max-model-len 16384
Runs the original weights, not the Q4_K_M GGUF; needs ~78 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model pherber3/Qwen3-Omni-30B-A3B-Instruct-4bit-mlx
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model pherber3/Qwen3-Omni-30B-A3B-Instruct-4bit-mlx

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
A40 (48 GB)CheapestCheapest20 GB free	~143 tok/s	$0.68	Deploy on RunPodfrom $0.35/hr (opens in new tab)	48 GB · 20 GB free · ~143 tok/s · $0.68 / 1M tokens
RTX A6000 (48 GB)20 GB free	~151 tok/s	$0.74	Deploy on Vast.aifrom $0.40/hr (opens in new tab)	48 GB · 20 GB free · ~151 tok/s · $0.74 / 1M tokens
A100 40GB12 GB free	~217 tok/s	$0.78	Deploy on Vast.aifrom $0.61/hr (opens in new tab)	12 GB free · ~217 tok/s · $0.78 / 1M tokens
A40 (48 GB)20 GB free	~143 tok/s	$1.28	Deploy on Vast.aifrom $0.66/hr (opens in new tab)	48 GB · 20 GB free · ~143 tok/s · $1.28 / 1M tokens
RTX 6000 Ada (48 GB)20 GB free	~172 tok/s	$1.08	Deploy on Vast.aifrom $0.67/hr (opens in new tab)	48 GB · 20 GB free · ~172 tok/s · $1.08 / 1M tokens
L40 (48 GB)20 GB free	~162 tok/s	$1.18	Deploy on RunPodfrom $0.69/hr (opens in new tab)	48 GB · 20 GB free · ~162 tok/s · $1.18 / 1M tokens
RTX 6000 Ada (48 GB)20 GB free	~172 tok/s	$1.20	Deploy on RunPodfrom $0.74/hr (opens in new tab)	48 GB · 20 GB free · ~172 tok/s · $1.20 / 1M tokens
L40S (48 GB)20 GB free	~162 tok/s	$1.35	Deploy on RunPodfrom $0.79/hr (opens in new tab)	48 GB · 20 GB free · ~162 tok/s · $1.35 / 1M tokens

Compare GPUs →

Your own private copy of Qwen3 Omni 30B A3B Instruct · Q4_K_M · OpenAI-compatible API

Open the llama.cpp listing on RunPod
Open on RunPodServerless (opens in new tab)
New to RunPod? Sign up and add credit first.
2
Fill in the form
Press Deploy and fill in:
Model
ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF
Quantization
Q4_K_M
Context Size
16384
After pasting the model press Esc — don’t pick a suggestion. Press Next.
Create the endpoint
Untick 24 GB, keep 48 GB, then Create Endpoint. Right away: Manage → Edit endpoint → Max workers 1 → Save (changing settings later restarts the worker).
The first start downloads the model, usually 5–15 min. After that, replies after a pause come in seconds.
Create an API key
RunPod → Settings → API Keys → Create. Pick “Restricted” with access to this endpoint only.

Use it

Copy the endpoint URL from its page on RunPod (any of its links works) and paste it with the key. Both stay in this browser.

Endpoint URL

API key

Chats and keys stay only in this browser.

https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1

Put your endpoint ID in place of <ENDPOINT_ID>. The API key is the one from step 2.

from openai import OpenAI
client = OpenAI(base_url="https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1", api_key="rpa_...")
r = client.chat.completions.create(
    model="any",
    messages=[{"role": "user", "content": "Hello!"}])
print(r.choices[0].message.content)

Works with any OpenAI client.

https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1

Put your endpoint ID in place of <ENDPOINT_ID>. The API key is the one from step 2.

Free on Windows, macOS, Linux, iOS, Android and the web.

Open the web app ↗ (opens in a new tab)Download ↗ (opens in a new tab)

1. Settings → Model Provider → Add → Add Custom Provider
2. API Mode OpenAI API Compatible; paste the API key and the base URL as API Host
3. Model → Fetch, pick the model
4. New chat → pick the provider → start chatting

Free and open source for Windows, macOS and Linux.

Download Jan ↗ (opens in a new tab)

1.Settings → next to Model Providers press +
2.API format OpenAI-compatible; Base URL is the base URL above (with /v1); API key is your key; Create
3.If no model appears, click + in Models and type any
4.New chat → pick the model → start chatting

How Much VRAM Does Qwen3 Omni 30B A3B Instruct Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
16.5 GB	24.8 GB	51.5 GB	~15 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Fits: 16K context · ~209 tok/s
18.9 GB	27.2 GB	53.9 GB	~17.2 GB	3-bit medium — 3-bit medium quantization	Fits: 16K context · ~196 tok/s
19.4 GB	27.7 GB	54.4 GB	~17.6 GB	4-bit · legacy — 4-bit legacy quantization	Fits: 16K context · ~194 tok/s
23.3 GB	31.6 GB	58.3 GB	18.6 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Fits: 4K context · ~177 tok/s
27.6 GB	36 GB	62.6 GB	~25.1 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 31.5 GB
32 GB	40.3 GB	67 GB	~29.1 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 35.9 GB
38.8 GB	47.1 GB	73.8 GB	32.5 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 42.7 GB
77.6 GB	85.9 GB	112.6 GB	61.1 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 81.5 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

27.2GBat 16K context

Weights: 22.4 GB
KV cache: 4.5 GB
Overhead: 0.3 GB

Fits RTX 4090 (reference) · fits up to 4K context · ~177 tok/s

≥50 tok/s needs ≥175 GB/s · RTX 5090 ~230, M3 Ultra ~77

Or .

Which Hardware Can Run Qwen3 Omni 30B A3B Instruct?

Q4_K_M · 23.3 GB

Qwen3 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. 100 devices with unified memory can run Qwen3 Omni 30B A3B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (36 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8–16 GB · RTX 4060, RTX 3060 12GB, RTX 5060 Ti 16GBDoesn't fit — smallest quant needs 20.4 GB8 GB tier →12 GB tier →16 GB tier →

24 GB · RTX 4090referenceQ4_K_M · 27.2 GB · ~177 tok/s · 4Ktight27.2~1774K24 GB tier →

Mac 32 GB · Mac Mini M4Q4_K_M · 27.2 GB · ~30 tok/s · 16K27.2~3016KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 27.2 GB · ~68 tok/s · 32K27.2~6832KMacs →

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6tok/s on RTX 4090

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Fine-tunes & variants(1)community builds of Qwen3 Omni 30B A3B Instruct

Clef Omni

Specifications

Publisher: Alibaba
Family: Qwen 3
Parameters: 35.3B
Active parameters: 3B per token
Architecture: Qwen3OmniMoeForConditionalGeneration
Release Date: 2025-09-20
License: Other

Resources

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)

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

Q4_K_M

23.3 GB

Learn more about VRAM estimation →

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_K

16.5 GB~75%

Q4_0

19.4 GB~85%

Q4_K_M ★

23.3 GB~89%

Q5_K_M

27.6 GB~92%

Q6_K

32.0 GB~95%

BF16

77.6 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen3 Omni 30B A3B Instruct on a Mac?

Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Qwen3 Omni 30B A3B Instruct, 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 23.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).

Can I run Qwen3 Omni 30B A3B Instruct locally?

Yes — Qwen3 Omni 30B A3B Instruct can run locally on consumer hardware. 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 = 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 → 2.0 GB active ÷ (8000 × 0.65) = 0.38 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 3.02 ms = ~331 tok/s

Estimated speed at Q4_K_M (23.3 GB)

NVIDIA B200

~331 tok/s

NVIDIA GeForce RTX 4090

~177 tok/s

NVIDIA B300

~331 tok/s

NVIDIA GH200 Grace Hopper Superchip

~307 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

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. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.

Which devices can run Qwen3 Omni 30B A3B Instruct?

100 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, 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.