refactor(recipes): add h100 recipe namespace (#4622)

Signed-off-by: yaoyu-33 <yaoyu.094@gmail.com>

Author: yaoyu-33

README.md

@@ -13,7 +13,7 @@
 ## 📣 News
 - [06/22/2026] **Megatron Bridge 0.5.0 released!** Highlights include expanded LLM and multimodal support (Qwen3.5, DeepSeek V4, Ernie 4.5, GLM-5/4.7, StepFun Step-3.5/3.7, MiMo-V2, Gemma 4, Falcon H1, Ling MoE V2, Nemotron-3 Nano Omni, Qwen3-Omni, Qwen3-ASR, and Nemotron Diffusion), MegatronMIMO and Energon v7 training updates, evaluator backend integration, eval-time context parallelism, deterministic recipes, quantized FP8/MXFP4 export, CUDA graph/performance improvements, and Megatron Inference/tokenizer unification with Megatron-LM. Huge thanks to our community contributors: [@HowardZorn](https://github.com/HowardZorn), [@hy2826](https://github.com/hy2826), [@bo-ke](https://github.com/bo-ke), [@beccohov](https://github.com/beccohov), [@dhiaEddineRhaiem](https://github.com/dhiaEddineRhaiem), [@pavelgein](https://github.com/pavelgein), [@ccclyu](https://github.com/ccclyu), [@hbhflw2000](https://github.com/hbhflw2000), and [@HollowMan6](https://github.com/HollowMan6)! See the [full release notes](https://github.com/NVIDIA-NeMo/Megatron-Bridge/releases/tag/v0.5.0).
 
-- [06/16/2026] NVIDIA topped [MLPerf Training v6.0](https://developer.nvidia.com/blog/nvidia-blackwell-tops-mlperf-training-6-0-with-industry-leading-scale-and-performance/) across every benchmark, including the new DeepSeek-V3 and GPT-OSS MoE training workloads. Megatron Bridge serves as the packaging layer for the NeMo 26.06 training stack that integrates full-iteration CUDA graphs, HybridEP/router optimizations, all-to-all overlap, MXFP8 attention, and pipeline-layout balancing; the blog highlights [DeepSeek-V3 training](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/scripts/performance/configs/deepseek) at **1,648 TFLOPS/GPU** (**6,338 tokens/sec/GPU**) on GB300. To try related runs, start from the [performance recipes](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/scripts/performance); the corresponding container is expected with the NeMo 26.06 release soon.
+- [06/16/2026] NVIDIA topped [MLPerf Training v6.0](https://developer.nvidia.com/blog/nvidia-blackwell-tops-mlperf-training-6-0-with-industry-leading-scale-and-performance/) across every benchmark, including the new DeepSeek-V3 and GPT-OSS MoE training workloads. Megatron Bridge serves as the packaging layer for the NeMo 26.06 training stack that integrates full-iteration CUDA graphs, HybridEP/router optimizations, all-to-all overlap, MXFP8 attention, and pipeline-layout balancing; the blog highlights [DeepSeek-V3 training](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/perf_recipes/deepseek) at **1,648 TFLOPS/GPU** (**6,338 tokens/sec/GPU**) on GB300. To try related runs, start from the [performance recipes](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/scripts/performance); the corresponding container is expected with the NeMo 26.06 release soon.
 
 - [06/04/2026] [**NVIDIA Nemotron 3 Ultra**](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16) is now public! Day-0 support for the 550B-A55B hybrid Mamba-Transformer MoE model is available on the [`nemotron_3_ultra`](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/nemotron_3_ultra) branch, including checkpoint conversion, inference, SFT, PEFT (LoRA), and pretraining examples. Read the [NVIDIA Technical Blog](https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/) and see the [examples README](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_ultra/examples/models/nemotron/nemotron_3/ultra/README.md) for the full walkthrough.
 
@@ -141,14 +141,14 @@ Training quickstart using pre-configured recipes:
 
 ```python
 from megatron.bridge import AutoBridge
-from megatron.bridge.recipes.llama import llama32_1b_pretrain_config
+from megatron.bridge.recipes.llama.h100 import llama32_1b_pretrain_1gpu_h100_bf16_config
 from megatron.bridge.training.gpt_step import forward_step
 from megatron.bridge.training.pretrain import pretrain
 
 if __name__ == "__main__":
     # The recipe uses the Llama 3.2 1B architecture from Hugging Face.
     # This is random-init pretraining and does not require a converted Megatron checkpoint.
-    cfg = llama32_1b_pretrain_config()
+    cfg = llama32_1b_pretrain_1gpu_h100_bf16_config()
 
     # The recipe already sets cfg.model internally using this pattern.
     # Override cfg.model to choose a different Hugging Face model ID as the architecture source.

docs/fern/versions/nightly/pages/megatron-lm-to-megatron-bridge.mdx

@@ -30,11 +30,11 @@ uv run python scripts/translate_mlm_to_bridge.py \
 ```bash
 # From a Bridge recipe name (defaults exported as MLM args)
 uv run python scripts/translate_mlm_to_bridge.py --reverse \
-    --recipe llama32_1b_pretrain_config
+    --recipe llama32_1b_pretrain_1gpu_h100_bf16_config
 
 # From a recipe plus inline overrides
 uv run python scripts/translate_mlm_to_bridge.py --reverse \
-    --recipe llama32_1b_pretrain_config \
+    --recipe llama32_1b_pretrain_1gpu_h100_bf16_config \
     --args "train.train_iters=1000 model.tensor_model_parallel_size=2"
 
 # From Bridge overrides only (no recipe)
@@ -99,7 +99,7 @@ Run the generic recipe launcher and override config keys directly:
 
 ```bash
 uv run python scripts/training/run_recipe.py \
-  --recipe llama3_8b_pretrain_config \
+  --recipe llama3_8b_pretrain_2gpu_h100_bf16_config \
   --dataset llm-pretrain \
   train.micro_batch_size=2 \
   train.global_batch_size=128 \

docs/fern/versions/nightly/pages/models/gemma/gemma2.mdx

@@ -166,7 +166,7 @@ config = gemma2_27b_pretrain_config(
 
 #### Gemma 2 2B
 ```bash
-uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=8 scripts/training/run_recipe.py \
 --pretrained-checkpoint /models/gemma-2-2b \
 --recipe gemma2_2b_sft_config \
 train.global_batch_size=64 \
@@ -188,7 +188,7 @@ config = gemma2_2b_sft_config(
 
 #### Gemma 2 9B
 ```bash
-uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=8 scripts/training/run_recipe.py \
 --pretrained-checkpoint /models/gemma-2-9b \
 --recipe gemma2_9b_sft_config \
 train.global_batch_size=64 \
@@ -198,7 +198,7 @@ checkpoint.save=$SAVE_DIR/gemma2_9b_finetune
 
 #### Gemma 2 27B
 ```bash
-uv run python -m torch.distributed.run --nproc-per-node=16 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=16 scripts/training/run_recipe.py \
 --pretrained-checkpoint /models/gemma-2-27b \
 --recipe gemma2_27b_sft_config \
 train.global_batch_size=64 \
@@ -210,7 +210,7 @@ checkpoint.save=$SAVE_DIR/gemma2_27b_finetune
 
 #### Gemma 2 2B
 ```bash
-uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=8 scripts/training/run_recipe.py \
 --pretrained-checkpoint /models/gemma-2-2b \
 --recipe gemma2_2b_peft_config \
 --peft_scheme lora \

docs/fern/versions/nightly/pages/models/gemma/gemma3.mdx

@@ -155,7 +155,7 @@ config = gemma3_1b_peft_config(
 
 **Full Finetuning:**
 ```bash
-uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=8 scripts/training/run_recipe.py \
   --pretrained-checkpoint /models/gemma-3-1b-it \
   --recipe gemma3_1b_sft_config \
   train.global_batch_size=64 \
@@ -165,7 +165,7 @@ uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
 
 **LoRA Finetuning:**
 ```bash
-uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=8 scripts/training/run_recipe.py \
   --pretrained-checkpoint /models/gemma-3-1b-it \
   --recipe gemma3_1b_peft_config \
   --peft_scheme lora \

docs/fern/versions/nightly/pages/models/glm/glm45.mdx

@@ -214,7 +214,7 @@ config = glm45_355b_peft_config(
 
 ```bash
 # GLM 4.5 Air - LoRA finetuning on single node (8 GPUs)
-uv run python -m torch.distributed.run --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nproc-per-node=8 scripts/training/run_recipe.py \
 --pretrained-checkpoint /models/glm45-air-106b \
 --recipe glm45_air_106b_peft_config \
 --peft_scheme lora \
@@ -223,7 +223,7 @@ train.train_iters=1000 \
 checkpoint.save=$SAVE_DIR/glm45_air_lora
 
 # GLM 4.5 355B - Full finetuning (256 GPUs)
-uv run python -m torch.distributed.run --nnodes=32 --nproc-per-node=8 run/run_recipe.py \
+uv run python -m torch.distributed.run --nnodes=32 --nproc-per-node=8 scripts/training/run_recipe.py \
 --pretrained-checkpoint /models/glm45-355b \
 --recipe glm45_355b_sft_config \
 train.global_batch_size=256 \

docs/fern/versions/nightly/pages/models/llama/llama3.mdx

@@ -86,7 +86,7 @@ See: [bridge.recipes.llama.llama3](../../apidocs/bridge/bridge.recipes.llama.lla
   - `llama32_1b_pretrain_config`, `llama32_3b_pretrain_config`: Llama 3.2 (1B, 3B)
   - `llama3_8b_pretrain_config`: Llama 3 8B with 8K context
   - `llama3_8b_16k_pretrain_config`, `llama3_8b_64k_pretrain_config`, `llama3_8b_128k_pretrain_config`: Llama 3 8B with extended context (16K/64K/128K)
-  - `llama3_8b_low_precision_pretrain_config`: Llama 3 8B with low precision (FP8/MXFP8/NVFP4)
+  - `llama3_8b_pretrain_2gpu_h100_fp8cs_config`, `llama3_8b_pretrain_2gpu_h100_fp8mx_config`, `llama3_8b_pretrain_2gpu_h100_nvfp4_config`: Llama 3 8B H100 low precision (FP8/MXFP8/NVFP4)
   - `llama3_70b_pretrain_config`, `llama3_70b_16k_pretrain_config`, `llama3_70b_64k_pretrain_config`: Llama 3 70B (8K/16K/64K context)
   - `llama31_8b_pretrain_config`, `llama31_70b_pretrain_config`, `llama31_405b_pretrain_config`: Llama 3.1 (8B/70B/405B, 128K context)
 

docs/fern/versions/nightly/pages/recipe-usage.mdx

@@ -12,7 +12,7 @@ This guide will cover the next steps to make use of a training recipe, including
 
 ## Choosing a recipe or a new config
 
-Start from an exported recipe when the model family and workflow already exist in `megatron.bridge.recipes`. Recipe functions such as `llama3_8b_pretrain_config`, `llama32_1b_sft_config`, and `qwen3_8b_peft_config` provide model, optimizer, scheduler, precision, dataset, logger, and checkpoint defaults in one `ConfigContainer`. Override those defaults for your dataset, checkpoint paths, run length, parallelism, or precision before creating a new recipe.
+Start from an exported recipe when the model family and workflow already exist in `megatron.bridge.recipes`. Hardware-specific H100 recipes such as `llama3_8b_pretrain_2gpu_h100_bf16_config`, `llama32_1b_sft_1gpu_h100_bf16_config`, and `qwen3_8b_peft_1gpu_h100_bf16_config` are the canonical recipe names and share the same `<model>_<task>_<num>gpu_<gpu>_<dtype>[_<variant>]_config` naming strategy as performance recipes. Other hardware-specific recipe namespaces will follow the same pattern as they are added. The older recipe names remain available as compatibility aliases. Override those defaults for your dataset, checkpoint paths, run length, parallelism, or precision before creating a new recipe.
 
 Create a new recipe or config when the base model architecture is not represented by an existing model provider, the checkpoint conversion needs a new bridge, the forward step or dataset provider is model-specific, or you need a reusable configuration that will be shared across jobs. If the Hugging Face model is already supported by `AutoBridge`, you usually only need to start from the closest recipe and override the model provider or `hf_path`.
 

docs/fern/versions/nightly/pages/training/data-preparation.mdx

@@ -41,8 +41,8 @@ dataset = GPTDatasetConfig(
 The CLI-friendly `data_path` field is converted to Megatron Core's `blend` field during config finalization. For weighted multi-dataset training, use either a flattened `data_path` list with weights and prefixes or set `blend`/`blend_per_split` directly.
 
 ```bash
-uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
-    --recipe llama32_1b_pretrain_config \
+uv run python -m torch.distributed.run --nproc_per_node=1 scripts/training/run_recipe.py \
+    --recipe llama32_1b_pretrain_1gpu_h100_bf16_config \
     --dataset llm-pretrain \
     dataset.data_path=/data/dclm/preprocessed_text_document \
     dataset.seq_length=8192
@@ -81,8 +81,8 @@ dataset = FinetuningDatasetConfig(
 Launch the generic recipe runner with the preloaded local JSONL dataset type:
 
 ```bash
-uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
-    --recipe llama32_1b_sft_config \
+uv run python -m torch.distributed.run --nproc_per_node=1 scripts/training/run_recipe.py \
+    --recipe llama32_1b_sft_1gpu_h100_bf16_config \
     --dataset llm-finetune-preloaded \
     dataset.dataset_root=/data/sft_jsonl \
     dataset.seq_length=4096 \
@@ -114,8 +114,8 @@ If `dataset_root` is omitted, the processed JSONL is cached under the NeMo datas
 The generic launcher provides preset Hugging Face text datasets through `--dataset llm-finetune`:
 
 ```bash
-uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
-    --recipe llama32_1b_peft_config \
+uv run python -m torch.distributed.run --nproc_per_node=1 scripts/training/run_recipe.py \
+    --recipe llama32_1b_peft_1gpu_h100_bf16_config \
     --dataset llm-finetune \
     dataset.dataset_name=gsm8k \
     checkpoint.pretrained_checkpoint=/checkpoints/base_model
@@ -128,8 +128,8 @@ VLM recipes usually use a dataset provider instead of `FinetuningDatasetConfig`.
 For Energon/WebDataset data, create tar shards plus `.nv-meta` metadata and pass the dataset root to the recipe provider:
 
 ```bash
-uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
-    --recipe qwen3_vl_8b_peft_energon_config \
+uv run python -m torch.distributed.run --nproc_per_node=1 scripts/training/run_recipe.py \
+    --recipe qwen3_vl_8b_peft_1gpu_h100_bf16_energon_config \
     --dataset vlm-energon \
     --step_func qwen3_vl_step \
     dataset.path=/data/vlm_energon \
@@ -143,8 +143,8 @@ For preloaded VLM JSON or JSONL, use records with `messages` or `conversations`
 ```
 
 ```bash
-uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
-    --recipe qwen3_vl_8b_peft_config \
+uv run python -m torch.distributed.run --nproc_per_node=1 scripts/training/run_recipe.py \
+    --recipe qwen3_vl_8b_peft_1gpu_h100_bf16_config \
     --dataset vlm-preloaded \
     --step_func qwen3_vl_step \
     dataset.train_data_path=/data/vlm/train.jsonl \

docs/fern/versions/nightly/pages/training/mixed-precision.mdx

@@ -129,12 +129,12 @@ cfg.mixed_precision = "bf16_with_nvfp4_mixed"
 cfg.checkpoint.pretrained_checkpoint = "/checkpoints/base_model"
 ```
 
-Some model families expose tuned NVFP4 recipes. For example, Llama 3 8B has a low-precision pretraining helper:
+Some model families expose tuned NVFP4 recipes. For example, Llama 3 8B has a hardware-specific low-precision pretraining recipe:
 
 ```python
-from megatron.bridge.recipes.llama import llama3_8b_low_precision_pretrain_config
+from megatron.bridge.recipes.llama.h100 import llama3_8b_pretrain_2gpu_h100_nvfp4_config
 
-cfg = llama3_8b_low_precision_pretrain_config("bf16_with_nvfp4_mixed")
+cfg = llama3_8b_pretrain_2gpu_h100_nvfp4_config()
 ```
 
 NVFP4 is related to FP8 in that both use Transformer Engine low-precision kernels and BF16 fallback for non-quantized state, but the configuration knobs are separate. Use FP8 recipes on Hopper or Blackwell when FP8 is the target precision; use NVFP4 recipes on Blackwell when the model and hardware have been validated for 4-bit training.

docs/megatron-lm-to-megatron-bridge.md

@@ -30,11 +30,11 @@ uv run python scripts/translate_mlm_to_bridge.py \
 ```bash
 # From a Bridge recipe name (defaults exported as MLM args)
 uv run python scripts/translate_mlm_to_bridge.py --reverse \
-    --recipe llama32_1b_pretrain_config
+    --recipe llama32_1b_pretrain_1gpu_h100_bf16_config
 
 # From a recipe plus inline overrides
 uv run python scripts/translate_mlm_to_bridge.py --reverse \
-    --recipe llama32_1b_pretrain_config \
+    --recipe llama32_1b_pretrain_1gpu_h100_bf16_config \
     --args "train.train_iters=1000 model.tensor_model_parallel_size=2"
 
 # From Bridge overrides only (no recipe)
@@ -99,7 +99,7 @@ Run the generic recipe launcher and override config keys directly:
 
 ```bash
 uv run python scripts/training/run_recipe.py \
-  --recipe llama3_8b_pretrain_config \
+  --recipe llama3_8b_pretrain_2gpu_h100_bf16_config \
   --dataset llm-pretrain \
   train.micro_batch_size=2 \
   train.global_batch_size=128 \