Qwen3-Next#

Qwen3-Next is an advanced MoE language model from Alibaba Cloud’s Qwen team designed for high-throughput inference with large total parameter counts and efficient per-token activation.

Task

Text Generation (MoE)

Architecture

Qwen3NextForCausalLM

Parameters

80B total / 3B active

HF Org

Qwen

Available Models#

  • Qwen3-Next-80B-A3B: 80B total parameters, 3B activated per token

Architecture#

  • Qwen3NextForCausalLM

Example HF Models#

Model

HF ID

Qwen3-Next 80B A3B Instruct

Qwen/Qwen3-Next-80B-A3B-Instruct

Example Recipes#

Recipe

Description

qwen3_next_te_deepep.yaml

SFT — Qwen3-Next with TE + DeepEP

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

Note

This recipe was validated on 4 nodes × 8 GPUs (32 H100s). See the Launcher Guide for multi-node setup.

3. Run the recipe from inside the repo:

automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_next_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:26.02.00

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_next_te_deepep.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning#

See the Large MoE Fine-Tuning Guide.

Hugging Face Model Cards#