LLMs · Alibaba

Qwen3-Omni 30B-A3B Instruct

Name: Qwen3-Omni 30B-A3B Instruct
Author: Alibaba

Parameters: 35B (3.3B active)
Architecture: Mixture of experts
Max context: 64K tokens
License: apache-2.0
Released: 2025-09

How to run it

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers.

huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Qwen3-Omni 30B-A3B Instruct

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

LM Studio

Runs the MLX build, typically 35–90% faster than GGUF for a model with 3.3B active parameters on Apple silicon.

Also good:

Windows PC, NVIDIA GPU

LM Studio

Point-and-click: search, download and chat in one window.

Also good:

Windows PC, AMD Radeon

Lemonade

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

Also good:

Linux, NVIDIA GPU

LM Studio

Point-and-click: search, download and chat in one window.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App	Type	Opens it as	Systems	Engine	Start
LM Studio	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX	Open in LM Studio
Ollama	CLI + API	GGUF from Hugging Face	macOSWindowsLinux	llama.cpp, MLX	`ollama run hf.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M`
llama.cpp	CLI + API	GGUF	macOSWindowsLinux	llama.cpp	`llama-server -hf ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M`
Jan	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX	Open in Jan
MLX LM	CLI + API	MLX	macOS	MLX	`mlx_lm.chat --model mlx-community/Qwen3-Omni-30B-A3B-Instruct-4bit`
vLLM	Server	Original weights	WSLWindowsLinux	vLLM	`vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct`
SGLang	Server	Original weights	WSLWindowsLinux	SGLang	`python3 -m sglang.launch_server --model-path Qwen/Qwen3-Omni-30B-A3B-Instruct`
Docker Model Runner	CLI + API	GGUF	macOSWindowsLinux	llama.cpp, vLLM	`docker model run hf.co/ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M`
Lemonade	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, ONNX Runtime GenAI	`lemonade pull ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF:Q4_K_M`
Unsloth Desktop	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX, stable-diffusion.cpp	Open in Unsloth Desktop
Atomic Chat	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX-VLM	Open in Atomic Chat
KoboldCpp	Desktop app	GGUF	macOSWindowsLinux	llama.cpp	Get KoboldCpp
Text Generation WebUI	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, Transformers, ExLlamaV3	Get Text Generation WebUI
llamafile	CLI + API	GGUF	macOSWindowsLinux	llama.cpp	Get llamafile
Msty Studio	Desktop app	GGUF or MLX	macOSWindowsLinux	Ollama, llama.cpp, MLX	Open in Msty Studio
Llama (macOS)	Desktop app	GGUF	macOS	llama.cpp	Open in Llama (macOS)
RecurseChat	Desktop app	GGUF	macOS	llama.cpp	Open in RecurseChat
Inferencer	Desktop app	MLX	macOS	MLX	Get Inferencer
node-llama-cpp	Library	GGUF	macOSWindowsLinux	llama.cpp	Get node-llama-cpp
exo	Server	MLX	macOSLinux	MLX	Get exo
PocketPal AI	Mobile app	GGUF	iOSAndroid	llama.cpp	App Store Google Play
ChatterUI	Mobile app	GGUF	Android	llama.cpp	Download the APK
LLM Farm	Mobile app	GGUF	iOS	llama.cpp	Not on the App Store or TestFlight for now (checked 2026-11-09).

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

Quantization	Quality	Weights	Total at 8K context	Total at 32K context	Download
`Q2_K`	Low	14.1 GB	15.5 GB	17.9 GB	GGUF files (opens in a new tab)
`Q3_K_M`	Low	17.3 GB	18.7 GB	21.1 GB	GGUF files (opens in a new tab)
`Q4_K_M`	Good	21.5 GB	22.9 GB	25.3 GB	GGUF (opens in a new tab)
`Q5_K_M`	High	25.0 GB	26.4 GB	28.9 GB	GGUF files (opens in a new tab)
`Q6_K`	High	28.9 GB	30.3 GB	32.7 GB	GGUF files (opens in a new tab)
`Q8_0`	Very high	37.4 GB	38.8 GB	41.2 GB	GGUF (opens in a new tab)
`F16`	Full	70.5 GB	71.9 GB	74.3 GB	GGUF (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Qwen3-Omni 30B-A3B Instruct

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7900 XT (launch price $899) at Q3_K_M.

Quality	Hardware	Quant	Memory	Speed	Runs
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX PRO 6000 Blackwell	`F16`	76.7 GB / 96.0 GB	92.9 tok/s	Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M1 Max (64 GB)	`Q6_K`	34.2 GB / 48.0 GB	67.3 tok/s	Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Max (64 GB)	`Q6_K`	34.2 GB / 48.0 GB	84.4 tok/s	Runs great
67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Pro (48 GB)	`Q5_K_M`	29.3 GB / 36.0 GB	57.7 tok/s	Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5090	`Q6_K`	31.9 GB / 32.0 GB	173 tok/s	Runs welltight fit
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Apple M2 Ultra (192 GB)	`F16`	83.4 GB / 144 GB	47.6 tok/s	Runs great
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Max (128 GB)	`Q8_0`	46.5 GB / 96.0 GB	66.5 tok/s	Runs great
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX Spark N1X (6144-core GPU) (128 GB)	`Q8_0`	47.0 GB / 102 GB	46.7 tok/s	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (256 GB)	`F16`	87.3 GB / 192 GB	48.5 tok/s	Runs great
58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 7900 XTX	`Q3_K_M`	19.9 GB / 24.0 GB	174 tok/s	Runs great

Check it on your own hardware

Run it on a cluster

Devices that can’t run Qwen3-Omni 30B-A3B Instruct alone can run it together. These example clusters do:

Four Raspberry Pi 5s (16 GB)4.0 tok/s at Q5_K_M with llama.cpp RPC

Try it with your own devices

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.