How 120B Parameters Still Start from One Simple Neural Network Flow

Read Time: 6 minutes | Last Updated: January 2025

Table of Contents

• Introduction
• The Myth of Complexity
• The Simple Foundation
• Breaking Down the Core Flow
• From Simple to Scale: The Real Challenge
• Why This Matters for Students
• The Engineering Marvel
• Conclusion

Introduction

I always hear "70B parameters," "120B parameters," "state-of-the-art LLMs," and it sounds almost mythical. Today something clicked for me.

We always hear phrases like "70B parameters," "120B parameters," "state-of-the-art LLMs," and it sounds almost mythical. But when you zoom in, the core idea is surprisingly simple.

It's just a flow of basic operations—the same foundation that powers every neural network, from a simple 10-neuron model to a massive 120B-parameter language model.

This post breaks down that fundamental flow and shows how something so simple at the micro level becomes so powerful at scale.

The Myth of Complexity

The math isn't as scary as people make it sound. The fundamentals are not complicated. But turning those fundamentals into a frontier-level LLM? That's where the real complexity begins.

The Simple Foundation

Every large model starts with this exact flow:

Input → Weights → Linear Combination → Activation → Loss → Gradients → Update

That is the exact foundation behind every "billion-parameter" model.

The Core Flow Breakdown:

Input | Weights | Linear Combination

Z = wx + b

| Activation

a = sigmoid(z)

| Loss (Binary Cross Entropy)

-y log(a) + (1-y) log(1-a)

| Gradients

∂L/∂w, ∂L/∂b

| Update Gradients

w = w - learning_rate * ∂L/∂w
b = b - learning_rate * ∂L/∂b

That's it. That's the foundation.

Flow Diagram

The complete neural network flow: From input through weights, linear combination, activation, loss calculation, gradients, and weight updates—the foundation of every AI model.

From Simple to Scale: The Real Challenge

Scaling this flow to 120B parameters is where complexity grows:

The Scaling Challenge

At the micro level:

• One neuron: simple math
• One layer: manageable
• One forward pass: straightforward

At 120B parameters:

• 120 billion weights to optimize
• Thousands of layers
• Distributed across hundreds of GPUs
• Months of training time
• Millions of dollars in compute

What Makes It Complex

1. Distributed Training

2. Splitting the model across multiple GPUs

3. Synchronizing gradients across nodes

4. Handling communication bottlenecks

5. Memory Management

6. Fitting 120B parameters in memory

7. Gradient checkpointing

8. Mixed precision training

9. Optimization Tricks

10. Learning rate schedules

11. Advanced optimizers (Adam, AdamW)
12. Gradient clipping

13. Weight initialization strategies

14. Hardware Requirements

15. Thousands of GPUs working in parallel

16. Specialized infrastructure

17. Power consumption measured in megawatts

18. Engineering Challenges

19. Fault tolerance
20. Data pipeline optimization
21. Model parallelism strategies
22. Debugging at scale

Why This Matters for Students

If you're learning:

The Fundamentals Are Not Complicated

• The core math is accessible
• Start with one neuron
• Build up layer by layer
• Understand the flow before scaling

The Complexity Is in the Engineering

• Scaling requires systems knowledge
• Distributed computing expertise
• Optimization techniques
• Infrastructure management

The Learning Path

1. Master the fundamentals (this flow)
2. Build small networks (1-10 layers)
3. Understand optimization (gradient descent variants)
4. Learn distributed systems
5. Study large-scale training techniques

The Engineering Marvel

What makes modern LLMs impressive isn't just the math—it's the engineering:

• Efficiency: Training a 120B model in months instead of years
• Reliability: Keeping thousands of GPUs running for weeks
• Optimization: Techniques that make training feasible
• Innovation: New architectures and training methods

The math is simple. The engineering is not.

Real-World Perspective

What You Can Build Today

With the fundamentals, you can:

• Build neural networks from scratch
• Understand how LLMs work internally
• Implement your own training loops
• Experiment with architectures

What Requires Scale

To train a 120B model, you need:

• $10-100M in compute resources
• Teams of engineers
• Months of development
• Specialized infrastructure

But you can understand how it works with just the fundamentals.

The Beautiful Simplicity

There's something elegant about this:

At the core: Simple mathematical operations
At scale: Systems that can understand language, generate code, and reason about complex problems

The same Z = wx + b that fits in a single line of code becomes the foundation for models that can:

• Write code
• Answer questions
• Generate creative content
• Solve complex problems

Conclusion

The fundamentals are not complicated. The core flow—input, weights, linear combination, activation, loss, gradients, update—is the same whether you're building a 10-neuron network or a 120B-parameter model.

The complexity comes from scaling: distributed training, memory management, optimization, and infrastructure. Understanding the fundamentals gives you the foundation to appreciate and work with large models.

Key Takeaways

• The math behind neural networks is accessible
• Every large model uses the same fundamental operations
• The challenge is in scaling, not the core concepts
• Start with fundamentals, then explore scaling techniques
• Engineering makes the difference at scale

For Students Starting Out

Don't be intimidated by "120B parameters." Start with one neuron, understand the flow, build small networks, and gradually explore larger systems. The fundamentals are your foundation—master them first, then tackle the engineering challenges of scale.

The journey from Z = wx + b to a 120B-parameter model is one of the most fascinating stories in modern AI. And it all starts with understanding that simple flow.

I'm learning the basics slowly and deliberately, and honestly… it's fascinating to see how something so "simple" at the micro level becomes so powerful at scale.

Interested in diving deeper into neural network fundamentals? Check out our guides on Advanced RAG Techniques and Traditional AI vs Generative AI Architectures to see how these fundamentals power modern AI systems.

Tags: #NeuralNetworks #DeepLearning #MachineLearning #LLM #AIEducation #Fundamentals #120BParameters #NeuralNetworkBasics #AILearning #DeepLearningFundamentals