> ## Documentation Index > Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Qwen3-Next 80B-A3B > Launch recipes for Qwen3-Next-80B-A3B-Thinking on the Megatron backend. ## 1. Model Introduction [Qwen3-Next](https://huggingface.co/collections/Qwen/qwen3-next) is Alibaba's next-generation Qwen architecture, swapping classical attention for a hybrid Gated DeltaNet + Full Attention design. **Key highlights:** * **Hybrid Attention**: combines Gated DeltaNet (linear attention) with Full Attention to handle context lengths up to 262 K tokens efficiently. * **Highly Sparse MoE**: 80 B total / 3 B active per token — drastically reduces FLOPs per token without sacrificing model capacity. * **Multi-Token Prediction (MTP)**: built-in MTP layer enables EAGLE-style speculative rollout out of the box. * **HuggingFace-wrapped Megatron backend**: miles loads the `Qwen/Qwen3-Next-80B-A3B` HF module as a Megatron stage without re-implementing GDN from scratch. ## 2. Supported Variants | Model | Active / Total | HF ID | | --------------------------- | -------------- | ------------------------------------------------------------------------------------------- | | Qwen3-Next-80B-A3B-Thinking | 3 B / 80 B | [Qwen/Qwen3-Next-80B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking) | ## 3. Environment Setup ### 3.1 Required env vars ```bash theme={null} export MASTER_ADDR= ``` `--topology 4node` needs `MASTER_ADDR` for the ray fan-out; `--topology single-node` needs nothing. Paths are Typer flags: `--model-dir` (default `/root/models`) for the staged checkpoint, `--data-dir` (default `/root/datasets`) for the datasets, `--output-dir` (default `/root/shared_data`) for what the run writes. On a multi-node run all three must be on a shared FS. ### 3.2 Download model + datasets ```bash theme={null} hf download Qwen/Qwen3-Next-80B-A3B-Thinking --local-dir /root/models/Qwen3-Next-80B-A3B-Thinking hf download --repo-type dataset zhuzilin/dapo-math-17k --local-dir /root/datasets/dapo-math-17k hf download --repo-type dataset zhuzilin/aime-2024 --local-dir /root/datasets/aime-2024 ``` ### 3.3 HF → Megatron `torch_dist` conversion ```bash theme={null} cd /root/miles MODEL_ARGS_LINE="$(python3 miles/utils/external_utils/model_args_utils.py qwen3-next-80B-A3B)" || exit 1 read -ra MODEL_ARGS <<< "${MODEL_ARGS_LINE}" PYTHONPATH=/root/Megatron-LM torchrun --nproc-per-node 8 \ tools/convert_hf_to_torch_dist.py \ ${MODEL_ARGS[@]} \ --hf-checkpoint /root/models/Qwen3-Next-80B-A3B-Thinking \ --save /root/models/Qwen3-Next-80B-A3B-Thinking_torch_dist ``` ## 4. Launch ### 4.1 Quick start ```bash theme={null} cd /root/miles export MASTER_ADDR=... python scripts/run_qwen3_next_80b_a3b.py --topology 4node # single 8-GPU node (6 GPUs train, 2 serve rollout) python scripts/run_qwen3_next_80b_a3b.py --topology single-node ``` ### 4.2 Multi-node fan-out `--topology 4node` performs ssh fan-out internally over `/root/mpi_rack_hostfile` — set `MASTER_ADDR` on the head node and the launcher reaches out to the workers. Pass `--no-join-ray-workers` when the ray cluster is already complete. `--topology single-node` never fans out. ## 5. Recipe Configuration ### 5.1 Parallelism Both layouts are one launcher, `scripts/run_qwen3_next_80b_a3b.py`, selected by `--topology`: | `--topology` | Backend | TP | PP | CP | EP | expert-TP | `max_tokens_per_gpu` | Actor GPUs | | ------------- | -------- | -- | -- | -- | -- | --------- | -------------------- | ---------- | | `4node` | Megatron | 2 | 4 | 2 | 8 | 1 | 8192 | 32 (4 × 8) | | `single-node` | Megatron | 1 | 6 | 1 | 1 | 1 | 2048 | 6 (1 × 6) | `4node` colocates the rollout engines on the training GPUs; `single-node` dedicates the remaining 2 GPUs of the node to rollout, which also shrinks every batch dimension. ### 5.2 Algorithm Both topologies use GSPO (`--advantage-estimator gspo --eps-clip 4e-4`); `--use-kl-loss` is not passed. ### 5.3 Rollout & SGLang `4node` enables EAGLE speculative rollout: ```bash theme={null} --rollout-num-gpus-per-engine 8 --sglang-mem-fraction-static 0.8 --sglang-ep-size 8 --sglang-cuda-graph-bs 1 2 4 8 16 24 ... 128 --sglang-speculative-algorithm EAGLE --sglang-speculative-num-steps 2 --sglang-speculative-eagle-topk 1 --sglang-speculative-num-draft-tokens 3 --sglang-enable-draft-weights-cpu-backup --sglang-max-running-requests 512 ``` `single-node` drops the EAGLE block and uses `--rollout-num-gpus-per-engine 2 --rollout-num-gpus 2 --sglang-mem-fraction-static 0.8 --sglang-ep-size 1`. ### 5.4 Optimizer Both topologies enable CPU Adam: ```bash theme={null} --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer ``` ### 5.5 Notable quirks * Gated DeltaNet (GDN) is loaded via the HuggingFace bridge; miles doesn't re-implement GDN in Megatron native code. ## 6. Pairs Well With * [Backends Beyond Megatron](/docs/advanced/architecture-support) * [Rollout Routing Replay (R3)](/docs/advanced/miles-router) * [Speculative Decoding](/docs/advanced/speculative-decoding)