AI Training FAQ — How to Train Models Efficiently
AI training FAQ: distributed training, frameworks, costs, time. How to train LLMs, computer vision models, and more.
How long does it take to train a large language model?
LLM training time (with H100 GPUs): (1) 7B model: 1-2 days on 64 GPUs (~$5K), (2) 13B model: 3-5 days on 128 GPUs (~$20K), (3) 70B model: 2-4 weeks on 256-512 GPUs (~$200K-500K), (4) 175B model: 4-8 weeks on 1,024+ GPUs (~$2M-5M). Time depends on dataset size, sequence length, and hyperparameters.
What is distributed training and why is it needed?
Distributed training splits model training across multiple GPUs because: (1) Models too large for single GPU (70B model = 140GB+ in FP16), (2) Faster training (8 GPUs = ~8x speedup), (3) Larger batch sizes for stability. Approaches: (1) Data parallelism, (2) Tensor parallelism, (3) Pipeline parallelism. Frameworks: Megatron-LM, DeepSpeed, FSDP.
What is the best framework for distributed LLM training?
Top distributed training frameworks: (1) Megatron-LM (NVIDIA) — best for very large models, (2) DeepSpeed (Microsoft) — ZeRO optimization, (3) PyTorch FSDP — native PyTorch, (4) Ray Train — distributed scaling, (5) HuggingFace Accelerate — easy to use. For 70B+ models, use Megatron-LM or DeepSpeed. For 7B-13B, FSDP is sufficient.
How much does it cost to train a GPT-4 sized model?
GPT-4 training cost estimate: $80-100M (OpenAI). Breakdown: (1) ~25,000 A100 GPUs for 90 days, (2) ~$0.8M/day in GPU costs, (3) Plus research, experimentation, failed runs. With H100 GPUs and better efficiency, today it would cost ~$30-50M. Harch Corp can provide the GPU infrastructure for such projects.
What is mixed precision training?
Mixed precision training uses FP16 or BF16 for forward/backward passes (faster, less memory) while keeping FP32 master weights (accuracy). Benefits: 2x speedup, 50% memory reduction, no accuracy loss. H100 also supports FP8 (8-bit) for 4x speedup with minimal accuracy loss. Enable with `torch.cuda.amp` in PyTorch.
What is gradient checkpointing and when should I use it?
Gradient checkpointing trades compute for memory: instead of storing all activations for backprop, recompute them when needed. Benefits: 3-5x larger models on same GPU. Cost: 20-30% slower training. Use when GPU memory is the bottleneck (large models, long sequences). Enable with `model.gradient_checkpointing_enable()` in HuggingFace.
How do I choose the right batch size for training?
Batch size selection: (1) Start with 32-64 per GPU, (2) Increase until GPU memory is 90% full, (3) Use gradient accumulation for effective larger batch, (4) Larger batch = faster but may need learning rate adjustment, (5) For LLMs: 4M tokens global batch size is common. Monitor loss stability — if loss diverges, reduce batch size.
What is learning rate warmup and why is it important?
Learning rate warmup gradually increases learning rate from 0 to target over first N steps (e.g., 2,000). Benefits: (1) Prevents early training instability, (2) Allows optimizer state to stabilize, (3) Critical for large batch training. Typical: linear warmup over 2K-10K steps, then cosine decay. Use `transformers.get_cosine_schedule_with_warmup`.
How do I prevent overfitting during training?
Prevent overfitting: (1) Use more training data, (2) Data augmentation (text: back-translation, paraphrasing), (3) Dropout (0.1 for LLMs), (4) Weight decay (0.01-0.1), (5) Early stopping on validation loss, (6) Regularization techniques, (7) Smaller model (if data is limited), (8) Transfer learning (fine-tune pre-trained model).
What is LoRA and when should I use it?
LoRA (Low-Rank Adaptation) freezes pre-trained weights and trains small adapter layers (rank 8-64). Benefits: (1) 10x less memory (train 7B model on 1 GPU), (2) 5x faster training, (3) Adapters are small (50MB vs 14GB). Use LoRA for: fine-tuning on specific tasks/domains. Use full fine-tuning for: major capability changes. QLoRA = LoRA + 4-bit quantization for even less memory.