Distributed Training

Training LLMs: The Power of Many 🚀

A Story of Teamwork

Imagine you have to read every book in the world’s largest library. Alone, this would take hundreds of years! But what if you had thousands of friends, each reading different books at the same time? You could finish in weeks!

That’s exactly how we train giant AI brains like GPT-4 or Claude. One computer isn’t enough. We need an army of computers working together. This is called Distributed Training.

🌟 The Big Picture

Training a Large Language Model (LLM) is like teaching a super-smart student by showing them trillions of sentences. This requires:

• Massive data (hundreds of terabytes)
• Huge models (billions of parameters)
• Enormous compute power (thousands of GPUs)

No single computer can handle this alone. So we split the work!

graph TD
    A["Giant AI Brain"] --> B["Too Big for One Computer!"]
    B --> C["Split Across Many Computers"]
    C --> D["Each Does Part of the Work"]
    D --> E["Combine Results Together"]
    E --> F["Smart AI Ready! 🎉"]

🎯 Distributed Training Strategies

Think of these as different ways to divide homework among friends.

1. Data Parallelism 📊

The Analogy: Imagine 8 friends each reading different chapters of the same textbook. At the end of each hour, everyone shares what they learned, and you all update your notes together.

How It Works:

1. Copy the model to every computer (GPU)
2. Split the training data into smaller pieces
3. Each GPU trains on its own piece
4. Sync the learning (gradients) across all GPUs
5. Everyone updates together!

Example:

• You have 1,000,000 training sentences
• You have 8 GPUs
• Each GPU gets 125,000 sentences
• All GPUs learn from different data simultaneously

GPU 1: Sentences 1-125,000
GPU 2: Sentences 125,001-250,000
GPU 3: Sentences 250,001-375,000
...and so on!

✅ Best for: When your model fits on one GPU, but you want faster training.

2. Model Parallelism 🧩

The Analogy: Your puzzle is SO big that no single table can hold it. So you put different sections of the puzzle on different tables, and people at each table work on their section.

How It Works:

1. Split the model itself across multiple GPUs
2. Each GPU holds a different piece of the brain
3. Data flows from one GPU to the next
4. Like an assembly line in a factory!

Example:

• A model has 100 layers
• You have 4 GPUs
• GPU 1 handles layers 1-25
• GPU 2 handles layers 26-50
• GPU 3 handles layers 51-75
• GPU 4 handles layers 76-100

graph LR
    A["Input"] --> B["GPU 1: Layers 1-25"]
    B --> C["GPU 2: Layers 26-50"]
    C --> D["GPU 3: Layers 51-75"]
    D --> E["GPU 4: Layers 76-100"]
    E --> F["Output"]

✅ Best for: When your model is too big for one GPU.

3. Pipeline Parallelism 🚂

The Analogy: Think of a train factory. Station 1 builds the engine, Station 2 adds the wheels, Station 3 paints it. While Station 1 works on Train #2’s engine, Station 2 is already adding wheels to Train #1!

How It Works:

1. Split the model into stages (like model parallelism)
2. Send multiple mini-batches through at once
3. While GPU 1 processes Batch 2, GPU 2 processes Batch 1
4. Keeps all GPUs busy!

Example:

Time 1: GPU1=Batch1, GPU2=idle, GPU3=idle
Time 2: GPU1=Batch2, GPU2=Batch1, GPU3=idle
Time 3: GPU1=Batch3, GPU2=Batch2, GPU3=Batch1
(Now all GPUs are working!)

✅ Best for: Reducing idle time when using model parallelism.

4. Tensor Parallelism ⚡

The Analogy: Imagine a giant math problem where you need to multiply huge tables of numbers. You split each table into 4 pieces, give each piece to a different friend, and combine the answers.

How It Works:

1. Split individual layers across GPUs (not the whole model)
2. Each GPU computes part of each layer
3. Results are combined within each layer
4. Requires lots of communication but very efficient!

Example:

• A single layer has a giant matrix of size 10,000 × 10,000
• Split it across 4 GPUs: each handles 10,000 × 2,500
• GPUs compute their pieces in parallel
• Results are gathered and combined

✅ Best for: Very large layers that don’t fit on one GPU.

5. ZeRO (Zero Redundancy Optimizer) 🧠

The Analogy: Instead of everyone carrying a full copy of the textbook, each friend carries only a few chapters. When someone needs a chapter they don’t have, they ask a friend who has it.

The Problem ZeRO Solves:

In data parallelism, every GPU stores:

• The full model
• All the optimizer states (like Adam’s momentum)
• All the gradients

This wastes SO much memory!

ZeRO Stages:

Example with ZeRO-3:

• 8 GPUs, 8 billion parameter model
• Instead of each GPU holding all 8B parameters
• Each GPU holds only 1B parameters
• Parameters are gathered when needed, then freed

✅ Best for: Training models that seem “too big” for your hardware.

🛠️ Distributed Training Frameworks

These are the tools that make distributed training possible.

PyTorch Distributed (DDP & FSDP) 🔥

The Analogy: If distributed training is a team sport, PyTorch Distributed is like having a great coach who tells everyone where to stand and when to pass the ball.

DistributedDataParallel (DDP)

• The go-to for data parallelism
• Automatically syncs gradients
• Works across multiple GPUs and machines

# Simple DDP example
import torch.distributed as dist
from torch.nn.parallel import DDP

model = MyBigModel()
model = DDP(model)  # Wrap it!
# Now training syncs automatically

Fully Sharded Data Parallel (FSDP)

• PyTorch’s answer to ZeRO
• Shards model, optimizer, and gradients
• Can train 10× larger models!

Example: Meta used FSDP to train LLaMA models!

DeepSpeed 🚀

The Analogy: DeepSpeed is like a turbo boost for your training car. It has all the ZeRO tricks plus extra speed features.

Key Features:

1. ZeRO Stages 1, 2, 3 - Memory efficiency
2. ZeRO-Offload - Use CPU RAM when GPU memory is full
3. ZeRO-Infinity - Even use NVMe SSDs for storage!
4. Mixed Precision - Train faster with FP16/BF16

Example:

# DeepSpeed makes it easy!
import deepspeed

model, optimizer, _, _ = deepspeed.initialize(
    model=model,
    config="ds_config.json"
)

Real-World: Microsoft trained their models using DeepSpeed, including early GPT experiments!

Megatron-LM 🤖

The Analogy: If you’re building the biggest skyscraper ever, you need specialized construction equipment. Megatron-LM is that specialized equipment for LLMs.

Specializes In:

1. Tensor Parallelism - Split layers across GPUs
2. Pipeline Parallelism - Split model into stages
3. Sequence Parallelism - Even split the text sequences!

3D Parallelism:

Megatron-LM combines ALL THREE:

• Data parallelism ✓
• Tensor parallelism ✓
• Pipeline parallelism ✓

graph TD
    A["3D Parallelism"] --> B["Data Parallel"]
    A --> C["Tensor Parallel"]
    A --> D["Pipeline Parallel"]
    B --> E["Different Data Batches"]
    C --> F["Split Each Layer"]
    D --> G["Split Model Stages"]

Example: NVIDIA trained Megatron-Turing (530B parameters) using this!

Ray Train 🌟

The Analogy: Ray is like having a super-smart assistant who handles all the boring scheduling and coordination so you can focus on the actual training.

What Makes Ray Special:

1. Framework Agnostic - Works with PyTorch, TensorFlow, JAX
2. Elastic Training - Add or remove GPUs mid-training!
3. Fault Tolerance - If one machine dies, training continues
4. Easy Scaling - Same code works on 1 GPU or 1,000 GPUs

Example Use Case:

• Start training on 16 GPUs
• Your cloud gives you 8 more? Ray adds them automatically!
• A machine crashes? Ray restarts that work on another machine!

Horovod 📡

The Analogy: Horovod is like a super-efficient postal service for AI training. It delivers gradients between computers using the fastest routes possible.

Key Feature: Ring-AllReduce

Instead of sending all gradients to one place:

• GPUs form a ring
• Each sends a piece to its neighbor
• After N steps, everyone has the full result!

GPU 0 → GPU 1 → GPU 2 → GPU 3
  ↑                        ↓
  ←←←←←←←←←←←←←←←←←←←←←←←←

Developed By: Uber, now used worldwide!

🌍 Training at Scale

Let’s see how the big players train their massive models.

Hardware Infrastructure 🖥️

GPU Clusters

• NVIDIA A100/H100 - The workhorses of AI training
• Thousands of GPUs working together
• Connected by super-fast networks

Networking

• InfiniBand - 400+ Gbps between machines
• NVLink - 900 GB/s between GPUs in same machine
• RoCE - Faster than regular Ethernet

Example Setup:

Meta's RSC (Research SuperCluster):
- 16,000 A100 GPUs
- 760 NVIDIA DGX A100 systems
- InfiniBand connections everywhere

Checkpointing Strategies 💾

The Problem: What if your training crashes after 2 weeks? Do you start over?

The Solution: Save your progress regularly!

Types of Checkpoints:

1. Full Checkpoints - Save everything (model + optimizer + state)
2. Sharded Checkpoints - Each GPU saves its own piece
3. Async Checkpoints - Save while training continues

Example:

# Save every 1,000 steps
if step % 1000 == 0:
    save_checkpoint(model, optimizer, step)

Real Cost: For a 175B model, a checkpoint can be 350GB!

Handling Failures 🛡️

The Reality: When you run 10,000 GPUs for weeks, something WILL break.

Common Failures:

• GPU dies (hardware failure)
• Network hiccup (connection lost)
• Machine restarts (software crash)

Solutions:

1. Redundancy - Extra machines ready to jump in
2. Automatic Restart - Detect failure, reload checkpoint, continue
3. Gradient Accumulation - If one batch fails, skip it safely
4. Elastic Training - Adjust to fewer/more GPUs dynamically

Example: Google’s TPU pods automatically replace failed chips!

Real-World Training Examples 🏆

GPT-3 (175B Parameters)

• Hardware: Thousands of NVIDIA V100 GPUs
• Training Time: ~34 days
• Cost: ~$4.6 million
• Strategy: Data parallelism + Model parallelism

LLaMA 2 (70B Parameters)

• Hardware: 2,000 A100 GPUs
• Training Time: ~21 days
• Tokens: 2 trillion
• Strategy: FSDP (Fully Sharded Data Parallel)

PaLM (540B Parameters)

• Hardware: 6,144 TPU v4 chips
• Training Time: ~60 days
• Strategy: Data + Model parallelism across TPU pods

graph TD
    A["Massive Training"] --> B["Thousands of GPUs/TPUs"]
    B --> C["Multiple Parallelism Strategies"]
    C --> D["Weeks of Training"]
    D --> E["Trillions of Tokens Processed"]
    E --> F["Powerful LLM Born! 🎉"]

The Cost of Scale 💰

Training giant models isn’t cheap!

What You’re Paying For:

• GPU/TPU hours (electricity + rental)
• Engineering team time
• Failed experiments (lots of them!)
• Data preparation and storage

Key Takeaways 🎓

1. No single computer can train modern LLMs - We need armies of GPUs working together.

2. Different parallelism strategies solve different problems:

   • Data Parallelism → Faster training
   • Model Parallelism → Bigger models
   • Pipeline Parallelism → Less idle time
   • ZeRO → Maximum memory efficiency

3. Frameworks make it possible:

   • DeepSpeed for memory tricks
   • Megatron-LM for 3D parallelism
   • PyTorch FSDP for simplicity

4. Training at scale requires:

   • Specialized hardware (GPU clusters)
   • Fast networks (InfiniBand/NVLink)
   • Robust checkpointing
   • Failure recovery systems

5. It’s expensive! Training GPT-4 cost more than most houses!

Your Journey Continues 🚀

Now you understand how the biggest AI brains are trained! From splitting work across thousands of GPUs to handling failures and saving progress, distributed training is the secret sauce behind every modern LLM.

Remember: Even the mightiest AI started with someone figuring out how to make many computers work as one.

You’ve got this! 💪