LLM Training Infrastructure: Building an AI Training Factory

Training large-scale language models requires powerful compute infrastructure. From hardware selection to software optimization, every step impacts training efficiency and cost. This article explores how to build an efficient training system.

Hardware Infrastructure

🖥️ GPU Selection

  • • NVIDIA A100/H100
  • • AMD MI250X
  • • Google TPU v4
  • • Domestic chip solutions

🔗 Network Architecture

  • • InfiniBand high-speed interconnect
  • • NVLink GPU interconnect
  • • RoCE over Ethernet
  • • Topology optimization

💾 Storage Systems

  • • Parallel file systems
  • • High-speed SSD arrays
  • • Distributed object storage
  • • Caching layer design

❄️ Cooling and Power

  • • Liquid cooling system design
  • • Power redundancy
  • • PUE optimization
  • • Data center planning

GPU Cluster Architecture

Typical Training Cluster Configurations

Scale# of GPUsCompute (PFLOPS)Memory (TB)Model Size
Small cluster8–16 GPUs2.5–50.64–1.287B parameters
Medium cluster64–256 GPUs20–805–2070B parameters
Large cluster1024+ GPUs300+80+175B+ parameters

Cluster Topology Design

# DGX SuperPOD architecture example
class GPUClusterTopology:
    def __init__(self):
        self.compute_nodes = {
            'dgx_a100': {
                'gpus_per_node': 8,
                'gpu_memory': '80GB',
                'cpu_cores': 128,
                'system_memory': '2TB',
                'nvlink_bandwidth': '600GB/s',
                'network': '8x200Gb InfiniBand'
            }
        }
        
    def calculate_cluster_specs(self, num_nodes):
        """Calculate cluster specs"""
        specs = {
            'total_gpus': num_nodes * 8,
            'total_gpu_memory': num_nodes * 8 * 80,  # GB
            'total_compute': num_nodes * 8 * 312,    # TFLOPS
            'interconnect_bandwidth': num_nodes * 1.6  # TB/s
        }
        
        # Network topology
        if num_nodes <= 16:
            specs['topology'] = 'fat-tree'
        else:
            specs['topology'] = 'dragonfly+'
            
        return specs
    
    def optimize_placement(self, model_size, batch_size):
        """Optimize task placement"""
        # Parallelism strategy
        parallel_config = {
            'data_parallel': self.calculate_dp_groups(),
            'tensor_parallel': self.calculate_tp_groups(),
            'pipeline_parallel': self.calculate_pp_stages()
        }
        
        return parallel_config

Distributed Training Frameworks

Parallel Training Strategies

📊 Data Parallelism

# PyTorch DDP
model = DistributedDataParallel(
    model,
    device_ids=[local_rank],
    output_device=local_rank
)
  • • Full model on each GPU
  • • Shard data across GPUs
  • • Synchronize gradients

🔀 Tensor Parallelism

# Megatron-LM
tensor_parallel_size = 8
model = TensorParallelModel(
    base_model,
    tp_size=tensor_parallel_size
)
  • • Intra-layer parallelization
  • • Shard matrix operations
  • • Reduce memory usage

🔗 Pipeline Parallelism

# Pipeline Parallel
pipeline_stages = 4
model = PipelineParallel(
    model,
    num_stages=pipeline_stages
)
  • • Split model by layers
  • • Pipeline execution
  • • Improve GPU utilization

3D Parallelism Combinations

# 3D Parallelism configuration
parallel_config = {
    'data_parallel_size': 8,      # DP dimension
    'tensor_parallel_size': 4,    # TP dimension  
    'pipeline_parallel_size': 2,  # PP dimension
    'total_gpus': 64              # 8 * 4 * 2
}

# Compute model shard per GPU
model_params_per_gpu = total_params / (tp_size * pp_size)

Training Optimization Techniques

Performance Optimization Strategies

⚡ Mixed Precision Training

# Automatic Mixed Precision
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

with autocast():
    output = model(input)
    loss = criterion(output, target)
    
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()

Advantages:

  • • 2–3× training speedup
  • • 50% lower memory usage
  • • Maintain model accuracy

💾 ZeRO Optimizer

ZeRO-1

Optimizer state sharding

ZeRO-2

+ Gradient sharding

ZeRO-3

+ Parameter sharding

🚀 Communication Optimization

  • • Gradient compression to reduce traffic
  • • Overlap communication with compute
  • • NCCL optimizations for collectives
  • • Topology awareness for routing

Monitoring and Debugging

Training Monitoring System

Key Monitoring Metrics

🔍 Performance

  • • GPU utilization (target: > 95%)
  • • Memory usage
  • • Communication bandwidth usage
  • • FLOPS efficiency

📊 Training

  • • Loss curves
  • • Gradient norms
  • • Learning rate schedule
  • • Validation performance
# TensorBoard monitoring
tensorboard --logdir=./logs --port=6006

# NVIDIA Nsight Systems performance profiling
nsys profile -t cuda,nvtx,osrt,cudnn     -o profile_report     python train.py

Cost Optimization

Training Cost Analysis

Model SizeGPU hoursCloud costPower costTotal cost
7B parameters2,000$6,000$800$6,800
70B parameters50,000$150,000$20,000$170,000
175B parameters300,000$900,000$120,000$1,020,000

💰 Cost Optimization Strategies

  • • Use spot instances to save up to 70%
  • • Hybrid cloud strategy: on-prem + public cloud
  • • Resume from checkpoints to avoid retraining
  • • Parameter-efficient fine-tuning to reduce training

Best Practices

LLM Training Lessons Learned

✅ Pre-training Preparation

  • • Data quality checks and cleaning
  • • Small-scale experiments for validation
  • • Parallelism strategy optimization
  • • Deploy monitoring stack

🚀 During Training

  • • Dynamically adjust learning rate
  • • Save checkpoints frequently
  • • Monitor anomaly indicators
  • • Elastic scale up/down

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