Qwen3 MoE

Qwen3 MoE is the Mixture-of-Experts variant of the Qwen3 series from Alibaba Cloud, activating a small fraction of parameters per token for efficient large-scale training.


Task	Text Generation (MoE)
Architecture	`Qwen3MoeForCausalLM`
Parameters	30B – 235B total
HF Org	Qwen

Available Models

Qwen3-30B-A3B: 30B total parameters, 3B activated per token
Qwen3-235B-A22B: 235B total parameters, 22B activated per token

Architecture

Qwen3MoeForCausalLM

Example HF Models

Model	HF ID
Qwen3 30B A3B	`Qwen/Qwen3-30B-A3B`
Qwen3 235B A22B	`Qwen/Qwen3-235B-A22B`

Example Recipes

Recipe	Description
qwen3_moe_30b_te_deepep.yaml	SFT — Qwen3 MoE 30B with TE + DeepEP
qwen3_moe_30b_lora.yaml	LoRA — Qwen3 MoE 30B

Try with NeMo AutoModel

1. Install (full instructions):

$ pip install nemo-automodel

2. Clone the repo to get the example recipes:

$ git clone https://github.com/NVIDIA-NeMo/Automodel.git
$ cd Automodel

3. Run the recipe from inside the repo:

$ automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_moe_30b_te_deepep.yaml

Run with Docker

1. Pull the container and mount a checkpoint directory:

$ docker run --gpus all -it --rm \
>   --shm-size=8g \
>   -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
>   nvcr.io/nvidia/nemo-automodel:26.06.00

2. Navigate to the AutoModel directory (where the recipes are):

$ cd /opt/Automodel

3. Run the recipe:

$ automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_moe_30b_te_deepep.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning

See the LLM Fine-Tuning Guide and the Large MoE Fine-Tuning Guide.