---
name: recipe-recommender-e8bdd9259929
description: Recommend and customize Megatron Bridge recipes for a user's model, GPU count, and training goal. Indexes library recipes (pretrain/SFT/PEFT) and performance recipes.
when_to_use: User wants a starting recipe or training config; 'which recipe', 'recommend recipe', 'how to train Llama', 'starting config for X GPUs', 'what recipe for SFT'.
---

# Auto Recipe — Recipe Index & Recommendation

This skill indexes every shipped recipe and helps users pick the right starting
config, adjust parallelism, and avoid common pitfalls.

## How to Use This Skill

1. Ask the user for: **model name/size**, **GPU count & type**, **training goal**
   (pretrain / SFT / PEFT), and **sequence length** (if non-default).
2. Look up the best-match recipe in the index below.
3. Recommend the recipe function name + entry-point command.
4. Provide adjustment advice (parallelism resizing, batch tuning, pitfalls).

---

## Entry Points

### Library recipes (functional training)

```bash
# Pretrain with mock data
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe <recipe_function_name> \
    --dataset llm-pretrain-mock

# SFT with SQuAD
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe <recipe_function_name> \
    --dataset llm-finetune

# Override any field via CLI
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe llama3_8b_pretrain_config \
    --dataset llm-pretrain-mock \
    'model.tensor_model_parallel_size=2' \
    'training.global_batch_size=64'
```

### Performance recipes (throughput benchmarks)

```bash
python scripts/performance/run_script.py \
    --recipe <model_family> \
    --gpu_type h100 \
    --num_gpus 64 \
    --data mock
```

> **Perf recipes are NOT fully validated for correctness.** Most conversations
> and testing were on mock data. They are designed for **upper-bound throughput
> measurement**, not production training. Always validate loss curves and
> convergence independently.

---

## Recipe Unification (Coming Soon — PR #2803)

PR [#2803](https://github.com/NVIDIA-NeMo/Megatron-Bridge/pull/2803) is
unifying performance recipes into the same **Python function** format used by
library recipes. Key changes:

- Perf recipes move from `scripts/performance/configs/` → `src/megatron/bridge/recipes/<family>/<model>_perf.py`
- Each perf recipe becomes a **self-contained Python function** (e.g. `llama3_8b_h100_bf16_pretrain_config()`)
- The old `WorkloadBaseConfig` → `set_workload_base_configs` → `get_perf_optimized_recipe` pipeline is removed
- Shared helpers: `_benchmark_common()` (50 iters, timing, TE RNG), `_perf_precision()` (bf16 / fp8_cs / fp8_mx / nvfp4)

**Why Python, not YAML?** Previous YAML-based approaches had problems:
recipe logic was split across multiple indirection layers, configs were not
self-contained, and the two-level pipeline made maintenance and debugging
difficult. Python functions are explicit, greppable, and composable.

After #2803 lands, both library and perf recipes will be invocable through the
same `run_recipe.py` entry point.

---

## Library Recipe Index

All recipes live under `src/megatron/bridge/recipes/`. Each function returns a
`ConfigContainer` with model, training, optimizer, and data settings.

### Llama

| Recipe | Mode | TP | PP | CP | SP | GPUs (min) | Seq Len |
|--------|------|----|----|----|----|------------|---------|
| `llama2_7b_pretrain_config` | Pretrain | 2 | 1 | — | — | 2 | 4K |
| `llama3_8b_pretrain_config` | Pretrain | 2 | 1 | — | ✓ | 2 | 8K |
| `llama3_8b_16k_pretrain_config` | Pretrain | 2 | 1 | 2 | ✓ | 4 | 16K |
| `llama3_8b_64k_pretrain_config` | Pretrain | 2 | 1 | 4 | ✓ | 8 | 64K |
| `llama3_8b_128k_pretrain_config` | Pretrain | 2 | 1 | 8 | ✓ | 16 | 128K |
| `llama3_70b_pretrain_config` | Pretrain | 8 | 4 | — | ✓ | 32 | 8K |
| `llama3_70b_16k_pretrain_config` | Pretrain | 8 | 4 | 2 | ✓ | 64 | 16K |
| `llama3_70b_64k_pretrain_config` | Pretrain | 8 | 4 | 4 | ✓ | 128 | 64K |
| `llama31_405b_pretrain_config` | Pretrain | 8 | 16 | — | ✓ | 128 | 8K |
| `llama3_8b_sft_config` | SFT | 2 | 1 | — | ✓ | 2 | 8K |
| `llama3_70b_sft_config` | SFT | 4 | 4 | — | ✓ | 16 | 8K |
| `llama31_405b_sft_config` | SFT | 8 | 8 | — | ✓ | 64 | 8K |
| `llama3_8b_peft_config` | PEFT | 1 | 1 | — | — | 1 | 8K |
| `llama3_70b_peft_config` | PEFT | 2 | 4 | — | ✓ | 8 | 8K |
| `llama31_405b_peft_config` | PEFT | 4 | 8 | — | ✓ | 32 | 8K |

### Qwen2 / Qwen2.5

| Recipe | Mode | TP | PP | Sizes |
|--------|------|----|----|-------|
| `qwen2_*_{pretrain,sft,peft}_config` | All | 1–8 | 1–4 | 500M, 1.5B, 7B, 14B, 32B, 72B |
| `qwen25_*_{pretrain,sft,peft}_config` | All | 1–8 | 1–4 | 500M, 1.5B, 3B, 7B, 14B, 32B, 72B |

### Qwen3 (Dense)

| Recipe | Mode | TP | PP | CP | Sizes |
|--------|------|----|----|-----|-------|
| `qwen3_*_pretrain_config` | Pretrain | 1–8 | 1–2 | — | 600M–32B |
| `qwen3_*_sft_config` | SFT | 1–8 | 1–2 | — | 600M–32B |
| `qwen3_600m_sft_128k_config` | SFT | 1 | 1 | 8 | 600M (128K seq) |
| `qwen3_*_peft_config` | PEFT | 1 | 1 | — | 600M–32B |

### Qwen3 MoE

| Recipe | Mode | TP | PP | EP | CP | GPUs |
|--------|------|----|----|----|----|------|
| `qwen3_30b_a3b_pretrain_config` | Pretrain | 1 | 1 | 8 | — | 8 |
| `qwen3_30b_a3b_sft_config` | SFT | 1 | 1 | 8 | — | 8 |
| `qwen3_30b_a3b_peft_config` | PEFT | 1 | 1 | 1 | — | 1 |
| `qwen3_235b_a22b_pretrain_config` | Pretrain | 4 | 16 | 8 | 2 | 512+ |
| `qwen3_235b_a22b_sft_config` | SFT | 4 | 8 | 8 | — | 256 |
| `qwen3_235b_a22b_peft_config` | PEFT | 1 | 4 | 4 | — | 16 |

### Qwen3-Next

| Recipe | Mode | TP | PP | EP |
|--------|------|----|----|-----|
| `qwen3_next_80b_a3b_pretrain_config` | Pretrain | 1 | 4 | 8 |
| `qwen3_next_80b_a3b_sft_config` | SFT | 1 | 2 | 8 |
| `qwen3_next_80b_a3b_peft_config` | PEFT | 1 | 1 | 4 |

### DeepSeek

| Recipe | Mode | TP | PP | EP | GPUs |
|--------|------|----|----|-----|------|
| `deepseek_v2_lite_pretrain_config` | Pretrain | 1 | 1 | 8 | 8 |
| `deepseek_v2_pretrain_config` | Pretrain | 1 | 4 | 32 | 128 |
| `deepseek_v3_pretrain_config` | Pretrain | 2 | 16 | 64 | 2048 |
| `deepseek_v3_pretrain_config_32nodes` | Pretrain | 2 | 8 | 32 | 256 |

### GLM-4.5

| Recipe | Mode | TP | PP | EP | GPUs |
|--------|------|----|----|-----|------|
| `glm45_355b_pretrain_config` | Pretrain | 2 | 8 | 16 | 256 |
| `glm45_air_106b_pretrain_config` | Pretrain | 1 | 4 | 8 | 32 |
| `glm45_355b_sft_config` | SFT | 2 | 8 | 16 | 256 |
| `glm45_air_106b_sft_config` | SFT | 1 | 4 | 8 | 32 |
| `glm45_355b_peft_config` | PEFT | 2 | 4 | 4 | 32 |
| `glm45_air_106b_peft_config` | PEFT | 1 | 2 | 4 | 8 |

### Gemma

| Recipe | Mode | TP | PP | Sizes |
|--------|------|----|----|-------|
| `gemma2_*_{pretrain,sft,peft}_config` | All | 2–8 | 1–2 | 2B, 9B, 27B |
| `gemma3_1b_{pretrain,sft,peft}_config` | All | 1 | 1 | 1B (32K seq) |

### NemotronH / Nemotron

| Recipe | Mode | TP | PP | EP | Notes |
|--------|------|----|----|-----|-------|
| `nemotronh_{4b,8b,47b,56b}_*_config` | P/S/PEFT | 1–8 | 1–4 | — | Dense SSM-hybrid |
| `nemotron_3_nano_*_config` | P/S/PEFT | varies | 1 | 8 | MoE + Mamba |
| `nemotron_3_super_*_config` | P/S/PEFT | 4 | 1 | 8 | MoE + Mamba, ~40% CUDA graph gain |
| `nemotron_nano_{9b,12b}_v2_*_config` | P/S/PEFT | varies | 1 | — | Dense |

### Other Models

| Recipe | Mode | Notes |
|--------|------|-------|
| `moonlight_16b_{pretrain,sft,peft}_config` | All | MoE EP=8 |
| `olmoe_7b_{pretrain,sft,peft}_config` | All | MoE EP=8 |
| `ministral3_{3b,8b,14b}_{sft,peft}_config` | SFT/PEFT | Dense |
| `gpt_oss_20b_*_config` | All | MoE + FP8/MXFP8 variants |
| `gpt_oss_120b_*_config` | All | MoE |
| `vanilla_gpt_pretrain_config` | Pretrain | MLM/Bridge parity baseline |
| `gpt3_175b_pretrain_config` | Pretrain | TP=4, PP=8, VP=6 |
| `kimi_k2_pretrain_config` | Pretrain | 1T MoE, TP=2 PP=16 EP=32 |

### VLM Recipes

| Recipe | Mode | TP | PP | EP | GPUs |
|--------|------|----|----|-----|------|
| `gemma3_vl_{4b,12b,27b}_{sft,peft}_config` | SFT/PEFT | 1–8 | 1–2 | — | 1–16 |
| `qwen25_vl_{3b,7b,32b,72b}_{sft,peft}_config` | SFT/PEFT | 1–8 | 1–4 | — | 1–32 |
| `qwen3_vl_{8b,30b_a3b,235b_a22b}_{sft,peft}_config` | SFT/PEFT | 1–4 | 1–8 | 1–32 | 1–512 |
| `qwen35_vl_*_{sft,peft}_config` | SFT/PEFT | varies | varies | varies | varies |
| `glm_45v_{sft,peft}_config` | SFT/PEFT | 1 | 8 | 4–16 | 64–512 |
| `nemotron_nano_v2_vl_12b_{sft,peft}_config` | SFT/PEFT | 2–4 | 1 | — | 8 |

### Diffusion Recipes

| Recipe | Mode | TP | CP |
|--------|------|----|----|
| `wan_1_3B_{pretrain,sft}_config` | P/SFT | 1 | 8 |
| `wan_14B_{pretrain,sft}_config` | P/SFT | 2 | 4 |
| `flux_12b_{pretrain,sft}_config` | P/SFT | 2 | 1 |

---

## Performance Recipe Index

All perf recipes live under `scripts/performance/`. They are invoked via
`run_script.py` and use `WorkloadBaseConfig` presets per GPU type.

> **Important:** Perf recipes are designed for **upper-bound throughput
> benchmarks**, not production training. They run **50 iterations** on **mock
> data** by default. Throughput numbers are aspirational targets, not validated
> convergence configs.

### Llama 3 / 3.1

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| Llama 3 8B | 8 | H100, B200, B300, GB200, GB300, R100 | CUDA graphs (local), FSDP on GB variants |
| Llama 3 70B | 64 | H100, B200, B300, GB200, GB300 | TP comm overlap (userbuffers), FSDP, CUDA graphs |
| Llama 3.1 405B | 128–1024 | H100, B200, B300, GB200, GB300 | TP+CP comm overlap (userbuffers), FSDP, heavy PP/VP |

SFT/LoRA variants also exist (e.g. 8B SFT with packed sequences, 70B SFT on 32 GPUs).

### DeepSeek V3

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| DeepSeek V3 (671B MoE) | 256–1024 | H100, B200, B300, GB200, GB300 | HybridEP dispatcher, MLA recompute, CUDA graphs (TE scoped) |

### Qwen3 MoE

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| Qwen3 30B-A3B | 8–16 | H100, B200, B300, GB200, GB300 | MoE alltoall/flex dispatcher |
| Qwen3 235B-A22B | 64–256 | H100, B200, B300, GB200, GB300 | TP comm overlap, CUDA graphs, MoE a2a overlap |
| Qwen3-Next 80B-A3B | 64–128 | H100, B200, B300, GB200, GB300 | EP 64–128 |

### Qwen3-VL

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| Qwen3-VL 30B-A3B | 8–16 | H100, B200, B300, GB200, GB300 | VLM + MoE |
| Qwen3-VL 235B-A22B | 64–256 | H100, B200, B300, GB200, GB300 | VLM + MoE, TP comm overlap |

### Kimi K2

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| Kimi K2 (1T MoE) | 256–1024 | H100, B200, B300, GB200, GB300 | Muon/Adam optimizer, HybridEP, pipeline layout helpers |

### NemotronH

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| Nemotron 3 Nano (30B MoE+Mamba) | 8–16 | H100, B200, B300, GB200, GB300 | TE CUDA graphs (attn+mamba+moe), HybridEP |
| Nemotron 3 Super | 64 | H100, B200, B300, GB200, GB300 | TE CUDA graphs, EP=64 |
| NemotronH 56B | 64 | H100, B200, B300 | TP=2–8, TE graphs (mamba+attn) |

### GPT-OSS

| Model | GPUs | GPU Types | Key Features |
|-------|------|-----------|--------------|
| GPT-OSS 120B | 64 | H100, B200, GB200 | EP=64, HybridEP on GB200 |

---

## Recommendation Decision Tree

```text
User wants to train a model
│
├─ Know the model name?
│   ├─ Yes → Look up in Library Recipe Index above
│   │   ├─ Has a recipe for their size + mode? → Use it directly
│   │   └─ No exact match? → Use closest size, adjust parallelism
│   └─ No → Ask for model name, size, and HF model ID
│
├─ What's the training goal?
│   ├─ Pretrain → Use *_pretrain_config
│   ├─ SFT (full fine-tune) → Use *_sft_config
│   └─ PEFT (LoRA/DoRA) → Use *_peft_config (lowest GPU requirement)
│
├─ How many GPUs?
│   ├─ 1 GPU → Only PEFT recipes work (TP=1, PP=1)
│   ├─ 8 GPUs (1 node) → Most 8B–16B models, small MoE (EP=8)
│   ├─ 16–64 GPUs → 70B dense, medium MoE
│   └─ 128+ GPUs → 405B+, large MoE (DeepSeek V3, Kimi K2)
│
├─ Want throughput benchmarks?
│   ├─ Yes → Use perf recipes (scripts/performance/)
│   │   └─ ⚠️ These run on mock data for upper-bound perf only
│   └─ No → Use library recipes (scripts/training/run_recipe.py)
│
└─ Long context?
    ├─ > 8K → Need CP (context parallelism), check *_16k / *_64k / *_128k variants
    └─ ≤ 8K → Default recipes work
```

---

## Adjustment Advice (When Recommending)

### Parallelism Resizing Rules

When the user's GPU count differs from the recipe default:

1. **TP must divide `num_key_value_heads`** (GQA constraint). E.g. if
   `num_key_value_heads=8`, valid TP = {1, 2, 4, 8}.
2. **TP should stay within a single node** (NVLink). TP > 8 requires
   inter-node NVLink (e.g., GB200 NVL72).
3. **PP adds pipeline bubbles.** Minimize PP; only increase when TP alone can't
   fit the model. Use VP (virtual pipeline) to mitigate bubble overhead.
4. **EP doesn't reduce dense-layer memory.** Only expert parameters shard with
   EP. Shared attention/embeddings are replicated. For "OOM with MoE", increase
   EP first, not TP.
5. **SP should be True whenever TP > 1.** It eliminates redundant activation
   copies and is essentially free.
6. **CP requires all-to-all or ring attention.** Check `cp_comm_type`. For
   GQA models, `a2a+p2p` hierarchical CP allows CP > num_kv_heads.
7. **world_size = DP × TP × PP × CP × EP.** DP is implicit. Make sure the
   product of explicit parallelisms divides your total GPU count.

### Batch Size Tuning

- Start with the recipe's `micro_batch_size`. If OOM, reduce to 1.
- `global_batch_size` determines learning dynamics. Scale with DP:
  `GBS = micro_batch_size × DP × gradient_accumulation_steps`.
- For MoE, `micro_batch_size=1` is typical at scale.

### Common Pitfalls to Warn About

| Pitfall | Symptom | Fix |
|---------|---------|-----|
| TP > num_kv_heads | Crash: "TP must divide num_query_groups" | Reduce TP to a divisor of num_kv_heads |
| PP without VP | Poor throughput (large bubble) | Set `virtual_pipeline_model_parallel_size` |
| EP too low for large MoE | OOM on expert params | Increase EP; each expert lives on EP/num_experts ranks |
| CUDA graphs + packed sequences | Assert: "CUDA graph accepts only Tensor inputs" | Disable packing or use `local` full-iteration graphs |
| CUDA graphs + full recompute | Assert: "full recompute only with full iteration CUDA graph" | Disable recompute or switch to `local` impl |
| `use_te_rng_tracker` not set | Assert on provider init when CUDA graphs enabled | Set `cfg.model.use_te_rng_tracker = True` and `cfg.rng.te_rng_tracker = True` |
| FSDP + TP > 1 on H100 | Possible comm bottleneck | Prefer FSDP with TP=1 or TP=2 on H100; FSDP shines on GB/B-series |
| Long context without CP | OOM on activations | Add CP=2/4/8; use `*_16k`, `*_64k`, or `*_128k` recipe variants |
| MoE `overlap_grad_reduce` on H100 | May hurt perf (False in many H100 presets) | Set `overlap_grad_reduce=False` for MoE on H100 |
| VLM SFT missing image data | Runs but produces garbage | Provide actual multimodal dataset or use mock VLM data |
| Qwen35-VL MoE FSDP | Tested on Blackwell only | May not work on H100; validate first |

### Recipe Override Examples

```bash
# Scale Llama3 8B from 2 GPUs to 8 GPUs (increase DP)
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe llama3_8b_pretrain_config \
    --dataset llm-pretrain-mock

# Reduce parallelism for Qwen3-MoE 30B to fit on 4 GPUs
uv run python -m torch.distributed.run --nproc_per_node=4 scripts/training/run_recipe.py \
    --recipe qwen3_30b_a3b_sft_config \
    --dataset llm-finetune \
    'model.expert_model_parallel_size=4'

# Add long context to an existing recipe
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe llama3_8b_pretrain_config \
    --dataset llm-pretrain-mock \
    'model.seq_length=32768' \
    'model.context_parallel_size=4'

# Enable CUDA graphs on any recipe
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe qwen3_30b_a3b_pretrain_config \
    --dataset llm-pretrain-mock \
    'model.cuda_graph_impl=transformer_engine' \
    'model.cuda_graph_scope=[attn,moe_router,moe_preprocess]' \
    'model.use_te_rng_tracker=True' \
    'rng.te_rng_tracker=True'
```

---

## Quick Reference: Which Recipe for My Situation?

| I want to... | Start with | GPUs needed |
|---|---|---|
| Try Bridge for the first time | `llama3_8b_sft_config` + mock data | 2 |
| Fine-tune a 7-8B model | `llama3_8b_sft_config` or `qwen3_8b_sft_config` | 2–8 |
| LoRA on 1 GPU | `llama3_8b_peft_config` or `qwen3_8b_peft_config` | 1 |
| Pretrain a dense 70B | `llama3_70b_pretrain_config` | 32–64 |
| Train a small MoE | `qwen3_30b_a3b_pretrain_config` | 8 |
| Train a large MoE (235B+) | `qwen3_235b_a22b_pretrain_config` | 256–512 |
| Benchmark throughput | Perf recipes via `run_script.py` | Varies |
| Long-context training | `llama3_8b_128k_pretrain_config` or add CP override | 16+ |
| VLM fine-tuning | `qwen3_vl_8b_sft_config` or `gemma3_vl_*_sft_config` | 4–8 |
| Diffusion training | `wan_1_3B_pretrain_config` or `flux_12b_pretrain_config` | 8 |

---

## Code Anchors

| What | Path |
|------|------|
| Library recipes root | `src/megatron/bridge/recipes/` |
| Recipe `__init__.py` (all exports) | `src/megatron/bridge/recipes/__init__.py` |
| Common recipe helpers | `src/megatron/bridge/recipes/common.py` |
| Training entry point | `scripts/training/run_recipe.py` |
| Perf recipes root | `scripts/performance/` |
| Perf entry point | `scripts/performance/run_script.py` |
| Perf workload configs | `scripts/performance/configs/<family>/` |
| Perf overrides (benchmark defaults) | `scripts/performance/utils/overrides.py` |


---
Source attribution: yo-steven
Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/Megatron-Bridge/recipe-recommender/SKILL.md
License: Apache-2.0
License information: https://www.apache.org/licenses/LICENSE-2.0
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

SPDX-License-Identifier: Apache-2.0 AND CC-BY-4.0

Copyright (c) 2026 NVIDIA Corporation

This project is licensed under a dual license:
- Apache License 2.0 (for code and configuration)
- Creative Commons Attribution 4.0 International (CC BY 4.0) (for documentation)

You may use the contents of this repository under the terms of either license,
or both, as applicable to the type of content.

================================================================================
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# Mega AI Hub resource metadata

Creator / publisher: yo-steven
Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/Megatron-Bridge/recipe-recommender/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: API
Tools: Megatron Bridge
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 027a1f99eb2a87ff84035176c13cff233b7ed79bcdaf8d3b77f286283ec8e8f2