> ## 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. # Nemotron-3-Nano MoE > Launch recipe for NVIDIA Nemotron-3-Nano-30B-A3B (Mamba+Attention+MoE hybrid) via Megatron AutoBridge. ## 1. Model Introduction [NVIDIA Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) is a hybrid Mamba + attention + MoE model. It pairs the `nemotron_h` block pattern from the dense 4B with a 128-expert sparse layer (top-6 routing, 1 shared expert, DSv3-style sigmoid routing with `routed_scaling_factor=2.5`). miles loads it through the `megatron.bridge` AutoBridge with a custom **NemotronH MoE bridge shim** (`miles_plugins/megatron_bridge/nemotron_h.py`) that wires `routed_scaling_factor`, `n_group`, and `topk_group` onto the Megatron provider. Without the shim the routed output is silently scaled 1.0× → \~0.28 logprob drift between train and rollout. **Key highlights:** * **Hybrid + MoE**: Mamba + attention + sparse MoE in the `nemotron_h` family. * **128 experts, top-6 routing**, 1 shared expert (3712-dim), aux-free expert-bias load balancing. * **Sigmoid routing** with `--moe-router-topk-scaling-factor 2.5`. * **Bridge-mode load**: `--megatron-to-hf-mode bridge` — no `torch_dist` conversion step. * **No RoPE**: `--position-embedding-type none`. ## 2. Supported Variants | Model | Active / Total | HF ID | | ----------------------- | -------------- | --------------------------------------------------------------------------------------------------------------- | | Nemotron-3-Nano-30B-A3B | 3 B / 30 B | [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) | ## 3. Environment Setup ### 3.1 Download model + datasets ```bash theme={null} hf download --repo-type dataset zhuzilin/dapo-math-17k --local-dir /root/datasets/dapo-math-17k hf download nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --local-dir /root/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 ``` Those are the `--data-dir` and `--model-dir` defaults; point them elsewhere with the flags. ### 3.2 No `torch_dist` conversion AutoBridge + the NemotronH MoE shim load the HF checkpoint directly. Both `--hf-checkpoint` and `--ref-load` point at the HF directory: ```bash theme={null} --hf-checkpoint /NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --ref-load /NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --save /checkpoints --save-interval 20 --megatron-to-hf-mode bridge ``` ## 4. Launch ### 4.1 Quick start ```bash theme={null} cd /root/miles python scripts/run_nemotron_3_nano.py --model-name NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 ``` `scripts/run_nemotron_3_nano.py` is shared with the dense [Nemotron-3-Nano-4B](/docs/models/nemotron/nemotron-3-nano); `--model-name` picks the MoE recipe here. It targets 1 node × 8 GPU (H200), default cell `TP=2 PP=2 EP=2`, and is a 10-step smoke test (`--num-rollout`). ## 5. Recipe Configuration ### 5.1 Parallelism Default cell is `TP=2 PP=2 EP=2`. Other verified cells from the upstream PR (10-step RL smoke, max logprob diff ≈ 0.014): | Cell | TP | PP | CP | EP | `max_tokens_per_gpu` | GPUs | | ---------------------- | -- | -- | -- | -- | -------------------- | --------- | | **default (launcher)** | 2 | 2 | 1 | 2 | 1024 | 8 (1 × 8) | | EP=4 | 1 | 1 | 1 | 4 | 1024 | 8 | | TP=2×EP=4+SP | 2 | 1 | 1 | 4 | 1024 | 8 | | PP=2×EP=4 | 1 | 2 | 1 | 4 | 1024 | 8 | | CP=2×EP=4 | 1 | 1 | 2 | 4 | 1024 | 8 | | TP=2×PP=2×EP=2+SP | 2 | 2 | 1 | 2 | 1024 | 8 | `--sequence-parallel` is enabled by the recipe. Activation checkpointing is on (`--recompute-granularity full --recompute-method uniform --recompute-num-layers 1`). `--log-probs-chunk-size 128` is required for the smoke memory budget. ### 5.2 Algorithm GRPO with low-variance KL: ```bash theme={null} --advantage-estimator grpo --use-kl-loss --kl-loss-coef 0.00 --kl-loss-type low_var_kl --entropy-coef 0.00 --eps-clip 0.2 --eps-clip-high 0.28 ``` ### 5.3 Rollout & SGLang ```bash theme={null} --rollout-num-gpus-per-engine 1 --sglang-mem-fraction-static 0.7 # Replay the exact rollout routing during training forward so # train logprobs match rollout logprobs (needed for MoE). --use-rollout-routing-replay ``` The `--use-rollout-routing-replay` flag is what keeps train and rollout logprobs aligned for the sigmoid-routed MoE — drop it and you'll see the same \~0.28 drift the bridge shim was added to fix. ### 5.4 Optimizer GPU Adam in the smoke recipe (no `--optimizer-cpu-offload`). Switch on CPU Adam if memory pressure rises. ### 5.5 Notable quirks From `scripts/models/nemotron-3-nano-30b-a3b.py` and `scripts/run_nemotron_3_nano.py`: * **No `--spec`**: AutoBridge + the NemotronH shim synthesize the Megatron MoE spec from HF config. * 128 experts, `--moe-router-topk 6`, shared expert (3712-dim). * Routing: `--moe-router-score-function sigmoid --moe-router-pre-softmax --moe-router-topk-scaling-factor 2.5`. * Group routing: `--moe-router-num-groups 1 --moe-router-group-topk 1` (no-op for `n_group=1`, kept for parity with HF config). * Aux-free balancing: `--moe-router-enable-expert-bias --moe-router-load-balancing-type seq_aux_loss --moe-router-bias-update-rate 0 --moe-aux-loss-coeff 0`. * `--moe-grouped-gemm`, `--moe-router-dtype fp32`. * `--position-embedding-type none`, `--vocab-size 131072 --make-vocab-size-divisible-by 128`. * `--attention-backend auto` (Mamba layers select their own kernel). See [Backends Beyond Megatron](/docs/advanced/architecture-support) for how the bridge shim layers `routed_scaling_factor` / `n_group` / `topk_group` onto the Megatron provider. ## 6. Pairs Well With * [Backends Beyond Megatron](/docs/advanced/architecture-support) * [P2P Weight Transfer](/docs/advanced/p2p-weight-transfer) * [Low Precision RL](/docs/advanced/low-precision)