Distributed Training Guide: Scale AI Training Across GPUs

What is Distributed Training?

Distributed training splits the work of training a neural network across multiple GPUs or machines. This enables training larger models and reduces training time from weeks to days.

Why Distributed Training?

• Train models too large for single GPU memory
• Reduce training time (8 GPUs ≈ 6-7x speedup)
• Use larger batch sizes for better convergence
• Scale to billion+ parameter models

Data Parallelism

The simplest form of distributed training. Each GPU has a copy of the full model and processes different batches of data. Gradients are synchronized after each step.

PyTorch DDP Example

# PyTorch DDP Example
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

# Initialize process group
dist.init_process_group(backend='nccl')

# Wrap model with DDP
model = DDP(model, device_ids=[local_rank])

# Training loop (same as single GPU)
for batch in dataloader:
    loss = model(batch)
    loss.backward()
    optimizer.step()

Pros

• Easy to implement
• Works with any model architecture
• Near-linear scaling to 8-16 GPUs

Cons

• Model must fit in GPU memory
• Communication overhead at scale
• Memory redundancy

Model Parallelism

When models are too large for a single GPU, you can split the model across multiple GPUs. Different strategies exist depending on how you split.

Data Parallelism

Same model on each GPU, different data batches

Best for: Models that fit in single GPU memory

Model Parallelism

Model split across GPUs, same data

Best for: Models too large for single GPU

Pipeline Parallelism

Model layers split across GPUs in pipeline

Best for: Very large transformer models

Tensor Parallelism

Individual layers split across GPUs

Best for: Large attention layers

Frameworks & Tools

Framework	Use Case	Difficulty
PyTorch DDP	Data parallelism	Easy
DeepSpeed	ZeRO optimization	Medium
FSDP	Fully sharded training	Medium
Megatron-LM	Large LLM training	Hard
Horovod	Multi-framework	Medium

DeepSpeed ZeRO Configuration

deepspeed_config.json

# DeepSpeed ZeRO-3 Config
{
  "zero_optimization": {
    "stage": 3,
    "offload_param": { "device": "cpu" },
    "offload_optimizer": { "device": "cpu" }
  },
  "fp16": { "enabled": true },
  "gradient_accumulation_steps": 4
}

Best Practices

Start with Data Parallelism

Use DDP first, only add complexity if needed

Profile before optimizing

Identify bottlenecks with PyTorch Profiler

Use gradient accumulation

Simulate larger batches without more memory

Enable mixed precision

FP16/BF16 reduces memory and speeds up training

Optimize communication

Use NCCL backend, overlap compute and communication

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