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64 GPUs total\nTP=4      # Tensor parallel\nPP=4      # Pipeline parallel\nCP=1      # Context parallel\n\n# LLaMA 70B configuration\nMODEL_SIZE=70  # Billion parameters\nHIDDEN_SIZE=8192\nNUM_LAYERS=80\nNUM_HEADS=64\nSEQ_LENGTH=4096\n\n# Training hyperparameters\nMICRO_BATCH=1\nGLOBAL_BATCH=1024\nLR=3e-4\n\ntorchrun \\\n  --nproc_per_node=$GPUS_PER_NODE \\\n  --nnodes=$NNODES \\\n  pretrain_gpt.py \\\n  --tensor-model-parallel-size $TP \\\n  --pipeline-model-parallel-size $PP \\\n  --context-parallel-size $CP \\\n  --sequence-parallel \\\n  --num-layers $NUM_LAYERS \\\n  --hidden-size $HIDDEN_SIZE \\\n  --num-attention-heads $NUM_HEADS \\\n  --seq-length $SEQ_LENGTH \\\n  --max-position-embeddings $SEQ_LENGTH \\\n  --micro-batch-size $MICRO_BATCH \\\n  --global-batch-size $GLOBAL_BATCH \\\n  --lr $LR \\\n  --train-iters 100000 \\\n  --lr-decay-style cosine \\\n  --lr-warmup-iters 2000 \\\n  --weight-decay 0.1 \\\n  --clip-grad 1.0 \\\n  --bf16 \\\n  --use-mcore-models \\\n  --transformer-impl transformer_engine \\\n  --data-path \u002Fpath\u002Fto\u002Fdata \\\n  --vocab-file \u002Fpath\u002Fto\u002Fvocab.json \\\n  --merge-file \u002Fpath\u002Fto\u002Fmerges.txt\n```\n\n**Step 3: Launch distributed training**\n\n```bash\n# Single node (8 GPUs)\nbash train_llama_70b.sh\n\n# Multi-node with SLURM\nsbatch --nodes=8 --gpus-per-node=8 train_llama_70b.sh\n```\n\n**Step 4: Monitor performance metrics**\n\nKey metrics to track:\n```\nModel FLOP Utilization (MFU): Target >40% on H100\nThroughput: Tokens\u002Fsec\u002FGPU\nMemory usage: \u003C80GB per GPU for 70B model\nLoss: Should decrease steadily\n```\n\n### Workflow 2: Configure Mixture of Experts (MoE) training\n\nFor sparse MoE models like Mixtral.\n\n```\nMoE Training:\n- [ ] Step 1: Configure expert parallelism\n- [ ] Step 2: Set MoE hyperparameters\n- [ ] Step 3: Launch training with EP\n```\n\n**Step 1: Configure expert parallelism**\n\n```bash\n# Mixtral 8x7B example\nTENSOR_PARALLEL=2\nPIPELINE_PARALLEL=1\nEXPERT_PARALLEL=4  # Split 8 experts across 4 GPUs\nDATA_PARALLEL=4\n\nTOTAL_GPUS=$((TENSOR_PARALLEL * PIPELINE_PARALLEL * EXPERT_PARALLEL * DATA_PARALLEL))\n# = 2 * 1 * 4 * 4 = 32 GPUs\n```\n\n**Step 2: Set MoE hyperparameters**\n\n```bash\ntorchrun \\\n  --nproc_per_node=8 \\\n  pretrain_gpt.py \\\n  --tensor-model-parallel-size 2 \\\n  --pipeline-model-parallel-size 1 \\\n  --expert-model-parallel-size 4 \\\n  --num-experts 8 \\\n  --moe-router-topk 2 \\\n  --moe-router-load-balancing-type aux_loss \\\n  --moe-aux-loss-coeff 0.01 \\\n  --hidden-size 4096 \\\n  --num-layers 32 \\\n  --num-attention-heads 32 \\\n  --seq-length 4096 \\\n  --max-position-embeddings 4096 \\\n  --bf16 \\\n  --use-mcore-models \\\n  --transformer-impl transformer_engine \\\n  --data-path \u002Fpath\u002Fto\u002Fdata \\\n  --vocab-file \u002Fpath\u002Fto\u002Fvocab.json \\\n  --merge-file \u002Fpath\u002Fto\u002Fmerges.txt\n```\n\n**Step 3: Launch training with EP**\n\nExpert parallelism distributes different experts across GPUs, reducing memory while maintaining capacity.\n\n```\nMemory without EP: 8 experts × 7B = 56GB per GPU\nMemory with EP=4: 2 experts × 7B = 14GB per GPU\nSavings: 75% memory reduction\n```\n\n### Workflow 3: Optimize for maximum throughput\n\nAchieve 47% MFU on H100.\n\n```\nPerformance Optimization:\n- [ ] Step 1: Enable Flash Attention\n- [ ] Step 2: Use FP8 precision (H100)\n- [ ] Step 3: Optimize micro-batch size\n- [ ] Step 4: Tune parallelism degrees\n```\n\n**Step 1: Enable optimizations**\n\n```bash\n--use-mcore-models  # Use Megatron Core models\n--transformer-impl transformer_engine  # Use Transformer Engine\n--sequence-parallel  # Reduce activation memory (use with TP)\n```\n\n**Step 2: Use FP8 precision (H100 only)**\n\n```bash\n--fp8-hybrid  # FP8 mixed precision training\n# Transformer Engine handles FP8 automatically\n```\n\nResult: 1.5-2x speedup on H100 vs BF16.\n\n**Step 3: Optimize micro-batch size**\n\nFind largest micro-batch that fits in memory:\n\n```bash\n# Start with 1, increase until OOM\nfor MBS in 1 2 4 8; do\n  echo \"Testing micro-batch-size=$MBS\"\n  torchrun ... --micro-batch-size $MBS\ndone\n```\n\nTypical values:\n- 7B model: 4-8\n- 70B model: 1-2\n- 405B model: 1\n\n**Step 4: Tune parallelism degrees**\n\nRules of thumb:\n```\nTensor Parallel: Use ≤8 (limited by NVLink within node)\nPipeline Parallel: Use for >70B models\nContext Parallel: Use for sequences >8K tokens\nData Parallel: Fill remaining GPUs\n```\n\nExample 405B on 128 H100s:\n```\nTP=8 (1 node)\nPP=8 (across nodes)\nCP=2 (long sequences)\nDP=1\nTotal = 8 × 8 × 2 × 1 = 128 GPUs\n```\n\n## When to use vs alternatives\n\n**Use Megatron-Core when:**\n- Training models >10B parameters\n- Need maximum efficiency (target >40% MFU)\n- Using NVIDIA GPUs (A100, H100)\n- Production training at scale\n- Want fine-grained parallelism control\n\n**Use alternatives instead:**\n- **PyTorch FSDP**: Models \u003C70B, simpler API, PyTorch native\n- **DeepSpeed**: Easier setup, good for \u003C100B models\n- **HuggingFace Accelerate**: Prototyping, simpler workflows\n- **LitGPT**: Educational, single-file implementations\n\n## Common issues\n\n**Issue: Low GPU utilization (\u003C30% MFU)**\n\nCauses:\n1. Micro-batch too small\n2. Too much parallelism overhead\n3. Not using Flash Attention\n\nFixes:\n```bash\n# Increase micro-batch\n--micro-batch-size 4  # Was 1\n\n# Enable optimizations\n--use-flash-attn\n--sequence-parallel\n\n# Reduce TP if >8\n--tensor-model-parallel-size 4  # Was 16\n```\n\n**Issue: Out of memory**\n\nReduce memory with:\n```bash\n--tensor-model-parallel-size 2  # Split model across GPUs\n--recompute-granularity full  # Gradient checkpointing\n--recompute-method block  # Checkpoint transformer blocks\n--recompute-num-layers 1  # Checkpoint every layer\n```\n\nOr use CPU\u002FNVMe offloading:\n```bash\n--cpu-optimizer  # Offload optimizer to CPU\n--cpu-optimizer-type ADAM  # CPU Adam variant\n```\n\n**Issue: Training slower than expected**\n\nCheck:\n1. **Network bottleneck**: Ensure InfiniBand\u002FNVLink enabled\n2. **Pipeline bubbles**: Use interleaved pipeline schedule\n   ```bash\n   --num-layers-per-virtual-pipeline-stage 2\n   ```\n3. **Data loading**: Use fast data loader\n   ```bash\n   --dataloader-type cyclic\n   ```\n\n**Issue: Diverging loss**\n\nStabilize training:\n```bash\n--lr-warmup-iters 2000  # Longer warmup\n--clip-grad 1.0  # Gradient clipping\n--init-method-std 0.006  # Smaller init\n--attention-dropout 0.0  # No dropout in attention\n--hidden-dropout 0.0  # No dropout in FFN\n```\n\n## Advanced topics\n\n**Parallelism strategies**: See [references\u002Fparallelism-guide.md](references\u002Fparallelism-guide.md) for detailed comparison of TP\u002FPP\u002FDP\u002FCP\u002FEP with performance analysis and when to use each.\n\n**Performance benchmarks**: See [references\u002Fbenchmarks.md](references\u002Fbenchmarks.md) for MFU numbers across different model sizes and GPU configurations.\n\n**Production configurations**: See [references\u002Fproduction-examples.md](references\u002Fproduction-examples.md) for real-world setups from LLaMA 3 405B, Nemotron-4 340B, and DeepSeek-V3 671B.\n\n**Training recipes**: See [references\u002Ftraining-recipes.md](references\u002Ftraining-recipes.md) 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