Distributed Training

Overview

Distributed training solves the fundamental constraint of single-device memory and compute by spreading neural network training across multiple accelerators. When a model has 175 billion parameters (like GPT-3), it requires ~700GB just to store weights in FP32—impossible on a single GPU with 80GB memory. Distributed training makes this feasible through parallelism strategies.

Key Parallelism Strategies

• **Data Parallelism**: Replicate model on each GPU, split batch across devices, synchronize gradients
• **Model Parallelism**: Split model layers across devices (layer 1-10 on GPU 0, 11-20 on GPU 1)
• **Pipeline Parallelism**: Split forward/backward passes into stages, overlap computation
• **Tensor Parallelism**: Split individual weight matrices across devices (most communication-intensive)
• **ZeRO (Zero Redundancy Optimizer)**: Partition optimizer states, gradients, and parameters to eliminate redundancy

Popular Frameworks

• **DeepSpeed**: Microsoft's distributed training library with ZeRO optimization, supports trillion-parameter models
• **PyTorch FSDP (Fully Sharded Data Parallel)**: Native PyTorch distributed training with automatic sharding
• **Megatron-LM**: NVIDIA's framework for training massive transformer models with 3D parallelism
• **Horovod**: Uber's framework for easy distributed training across TensorFlow, PyTorch, MXNet
• **JAX with pjit**: Google's approach using sharding annotations for automatic parallelism

Business Integration

Distributed training enables businesses to train custom models that were previously impossible. A financial services company training fraud detection on 10 years of transaction data (500TB) can use data parallelism across 64 GPUs to complete training in days instead of months. E-commerce companies training recommendation models with billions of parameters benefit from model parallelism to handle massive embedding tables. The key is choosing the right parallelism strategy: data parallelism for models that fit in memory, model/pipeline parallelism for models exceeding GPU memory.

Real-World Example: Custom LLM Training

A legal tech company needs to train a 30B parameter model on 200GB of legal documents. Using DeepSpeed ZeRO-3 across 8x A100 GPUs (640GB total memory), they achieve 150 tokens/second throughput with automatic gradient accumulation and mixed precision training. Training completes in 2 weeks instead of the 6 months it would take on a single GPU.

Implementation Example

Technical Specifications

• **Scaling Efficiency**: Linear scaling up to 1000+ GPUs with proper communication optimization
• **Memory Savings**: ZeRO-3 reduces memory per GPU by N× (N = number of GPUs)
• **Communication Overhead**: Data parallelism: ~10-20%, Pipeline parallelism: ~5-15%, Tensor parallelism: ~30-50%
• **Supported Hardware**: NVIDIA GPUs (NCCL), AMD GPUs (RCCL), Google TPUs (gRPC), AWS Trainium
• **Network Requirements**: InfiniBand or 100Gbps Ethernet for multi-node training

Best Practices

• Start with data parallelism for models <7B parameters that fit in single GPU memory
• Use FSDP/ZeRO for models 7B-70B parameters to reduce memory redundancy
• Combine pipeline + tensor parallelism for models >70B parameters
• Monitor GPU utilization and communication time—target >80% compute, <20% communication
• Use gradient checkpointing to trade compute for memory (2x slower, 10x less memory)
• Batch size should scale with number of GPUs to maintain statistical efficiency