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# 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.

> **Info**
>
> |                  |                                     |
> | ---------------- | ----------------------------------- |
> | **Task**         | Text Generation (MoE)               |
> | **Architecture** | `Qwen3NextForCausalLM`              |
> | **Parameters**   | 80B total / 3B active               |
> | **HF Org**       | [Qwen](https://huggingface.co/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`](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) |

## Example Recipes

| Recipe                                                                                                                                  | Description                       |
| --------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |
| [qwen3\_next\_te\_deepep.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/qwen/qwen3_next_te_deepep.yaml) | SFT — Qwen3-Next with TE + DeepEP |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
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](/job-launchers/slurm-cluster) for multi-node setup.

**3. Run the recipe** from inside the repo:

```bash
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:

```bash
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):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_next_te_deepep.yaml
```

See the [Installation Guide](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [Large MoE Fine-Tuning Guide](/recipes-e2e-examples/large-moe-fine-tuning).

## Hugging Face Model Cards

* [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct)