4 Ways to Make AI Models Cheaper, Faster, and Smaller (Without Losing What Makes Them Good)

4 Ways to Make AI Models Cheaper, Faster, and Smaller (Without Losing What Makes Them Good)

Published: January 29, 2026 - 12 min read

This is Part 10 of the Tokenomics for Humans series. If you haven't read Part 9 on Latency, Throughput, and RAG, I recommend starting there.

At the end of Part 9, I asked you to think about whether a smaller, faster model could handle 80% of what you do.

Today, we're going to explore how those smaller, faster models come to exist in the first place.

Because here's something most people don't realize: the AI models you use today didn't start out efficient. They were made efficient through a series of techniques that trade certain things for speed and cost savings.

Understanding these techniques helps you make smarter decisions about which AI to use and when.

Why Model Optimization Matters

In Part 4, I showed you the infrastructure behind AI: GPUs that cost $30,000-$40,000 each, data centers consuming electricity like small cities, and the token cost chain that connects your message to all of that hardware.

Here's the uncomfortable truth: the most capable AI models are also the most expensive to run.

They need:

• More memory (bigger GPUs)
• More computation (longer processing time)
• More electricity (higher operating costs)

So companies developed techniques to make AI models smaller and faster without completely destroying their capabilities.

These aren't theoretical. They're how GPT-4o mini exists. They're how you can run some AI models on your phone. They're how companies serve millions of users without going bankrupt.

Let's break down the four main techniques.

Technique 1: Quantization (Rounding the Numbers)

What is Quantization?

Inside every AI model are billions of numbers. These numbers are the "weights" that determine how the AI responds to different inputs.

By default, these numbers are stored with extreme precision. Something like:

3.141592653589793

Quantization reduces this precision:

Before quantization: 3.141592653589793
After quantization:  3.14

Less precision means:

• Smaller file size
• Faster calculations
• Less memory needed

The Measuring Cup Analogy

Imagine you're baking a cake.

High precision (original model): "Add exactly 236.588 milliliters of milk, heated to precisely 37.4 degrees Celsius."

Quantized (optimized model): "Add about 1 cup of warm milk."

For most cakes, the rounded version works perfectly fine. The difference is negligible in the final result, but the instruction is much simpler to follow.

AI quantization works the same way. For most tasks, slightly less precise numbers produce nearly identical results.

The Tradeoffs

QUANTIZATION TRADEOFFS
================================================================

WHAT YOU GAIN:
- Smaller model size (2-4x reduction)
- Faster inference (1.5-3x speedup)
- Runs on cheaper hardware
- Lower memory requirements

WHAT YOU MIGHT LOSE:
- Subtle accuracy on complex tasks
- Nuance in edge cases
- Some reasoning capability on difficult problems

================================================================
   Best for: Tasks where speed matters more than perfection
   Not ideal for: Complex reasoning, precise calculations
================================================================

Real-World Example

Let's say you have a customer service chatbot.

Without quantization:

• Requires expensive GPU with 80GB memory
• Costs $0.015 per response
• 2-second response time

With quantization:

• Runs on cheaper GPU with 24GB memory
• Costs $0.004 per response
• 0.8-second response time

For answering "Where's my order?" or "What's your return policy?", the quantized model performs identically. You just saved 73% on costs and made it faster.

Technique 2: Pruning (Trimming the Tree)

What is Pruning?

AI models have billions of connections between their internal components. Not all of these connections are equally important.

Pruning identifies and removes the connections that contribute least to the model's output.

Think of it like trimming a tree. You remove the branches that aren't productive so the whole tree can thrive with less effort.

The Neural Network Reality

Here's what happens inside an AI model:

BEFORE PRUNING
================================================================

     INPUT
       |
       v
  +---------+     +---------+     +---------+
  | Neuron  |---->| Neuron  |---->| Neuron  |
  +---------+     +---------+     +---------+
       |    \         |    \         |
       |     \        |     \        |
       v      v       v      v       v
  +---------+ +--+ +---------+ +--+ +---------+
  | Neuron  | |N | | Neuron  | |N | | Neuron  |
  +---------+ +--+ +---------+ +--+ +---------+
       |       |       |       |       |
       v       v       v       v       v
              ... thousands more layers ...

All connections are active. Many are barely contributing.

================================================================

AFTER PRUNING
================================================================

     INPUT
       |
       v
  +---------+     +---------+     +---------+
  | Neuron  |---->| Neuron  |---->| Neuron  |
  +---------+     +---------+     +---------+
       |              |                |
       |              |                |
       v              v                v
  +---------+     +---------+     +---------+
  | Neuron  |     | Neuron  |     | Neuron  |
  +---------+     +---------+     +---------+
       |              |                |
       v              v                v
              ... fewer connections ...

Weak connections removed. Model is smaller and faster.

================================================================

The Office Analogy

Imagine a company with 100 employees. After analysis, you discover:

• 30 employees do 80% of the critical work
• 50 employees contribute moderately
• 20 employees are barely producing anything

Pruning is like strategically reducing the workforce to the people who actually matter. The company becomes more efficient without losing its core capabilities.

(This is a metaphor about AI, not career advice. Please don't fire your employees or coworkers.)

The Tradeoffs

PRUNING TRADEOFFS
================================================================

WHAT YOU GAIN:
- Smaller model (30-90% size reduction possible)
- Faster inference
- Lower memory footprint
- Can enable running on edge devices

WHAT YOU MIGHT LOSE:
- Some general knowledge (pruned connections are gone forever)
- Flexibility on unexpected tasks
- Performance on tasks the model wasn't tested on during pruning

================================================================
   Best for: Specialized applications with predictable inputs
   Not ideal for: General-purpose AI needing broad capabilities
================================================================

Real-World Example

A medical imaging AI that detects tumors.

Before pruning:

• 175 billion parameters
• Requires specialized data center hardware
• Can also write poetry, translate languages, and discuss philosophy

After pruning:

• 12 billion parameters
• Runs on standard hospital equipment
• Only does tumor detection (but does it just as well)

For a specialized task, you don't need all those extra capabilities. Pruning removes what you don't need.

Technique 3: Knowledge Distillation (Teaching a Smaller Model)

What is Knowledge Distillation?

This is my favorite technique because the analogy is so intuitive.

Knowledge Distillation is when you train a smaller, simpler AI model to mimic a larger, more capable one.

The large model is the "teacher." The small model is the "student."

The student can't do everything the teacher can, but it can handle most tasks at a fraction of the cost.

The Chef Analogy

Imagine a world-renowned chef who charges $500 per dish. They've spent 30 years mastering every cuisine, every technique, every flavor combination.

Now imagine they train an apprentice. The apprentice watches, practices, and learns. After a year, the apprentice can make 90% of the dishes with 90% of the quality.

The apprentice charges $50 per dish.

For most customers, the apprentice is the better choice. Same great food (mostly), 90% cost savings.

That's knowledge distillation.

KNOWLEDGE DISTILLATION
================================================================

+-------------------------------------+
|         TEACHER MODEL               |
|   (e.g., GPT-4o, Claude Opus 4.5)   |
|                                     |
|   - Extremely capable               |
|   - Very expensive to run           |
|   - Hundreds of billions of params  |
+----------------+--------------------+
                 |
                 |  "Here's how I would
                 |   answer this question..."
                 |
                 v
+-------------------------------------+
|         STUDENT MODEL               |
|   (e.g., GPT-4o mini, Llama 8B)     |
|                                     |
|   - Learns teacher's behavior       |
|   - Much cheaper to run             |
|   - Billions (not hundreds) params  |
+-------------------------------------+

Result: Student achieves 80-95% of teacher's
performance at 10-20% of the cost.

================================================================

How It Actually Works

1. Generate training data: Run the teacher model on thousands of examples, recording both questions and answers.

2. Train the student: The smaller model learns to produce the same outputs as the teacher.

3. Test and refine: Compare student outputs to teacher outputs, adjusting until quality is acceptable.

The student isn't just memorizing. It's learning the patterns of how the teacher responds. This lets it generalize to new questions it's never seen before.

The Tradeoffs

DISTILLATION TRADEOFFS
================================================================

WHAT YOU GAIN:
- Much smaller model (10-20x reduction)
- Dramatically cheaper inference
- Faster response times
- Can run on consumer hardware

WHAT YOU MIGHT LOSE:
- Peak performance on hardest tasks
- Some nuance and sophistication
- Ability to handle rare edge cases
- The "magic" moments of exceptional insight

================================================================
   Best for: High-volume applications where 90% quality is fine
   Not ideal for: Tasks requiring the absolute best responses
================================================================

Real-World Example

This is literally how GPT-4o mini was created.

17x cheaper. For email drafting, code explanation, and general Q&A, the student performs nearly identically to the teacher.

Technique 4: Fine-Tuning (Specialization)

What is Fine-Tuning?

Fine-tuning is different from the other techniques. Instead of making a model smaller or faster, it makes a model better at specific tasks.

You take a general-purpose model and train it further on examples from your specific use case.

The Sports Analogy

Imagine a professional athlete who's generally good at all sports. They can play basketball, soccer, tennis, and swimming at a high level.

Fine-tuning is like having that athlete focus exclusively on tennis for six months. They become an exceptional tennis player, potentially at the cost of some basketball skills they had before.

The athlete didn't get bigger or smaller. They got specialized.

How Fine-Tuning Reduces Costs

"Wait," you might think. "If fine-tuning doesn't make the model smaller, how does it save money?"

Here's the trick: a specialized model often needs fewer tokens to produce good results.

BEFORE FINE-TUNING (General Model)
================================================================

You: "Write a legal contract for software licensing."

AI: "I'd be happy to help! A software licensing agreement
typically includes several key components. First, you'll
want to define the parties involved. Then, you should
specify the scope of the license. Additionally, consider
including provisions for..."

[Long, general explanation before getting to the actual contract]

Output: 2,000 tokens
================================================================

AFTER FINE-TUNING (Legal Specialist)
================================================================

You: "Write a legal contract for software licensing."

AI: "SOFTWARE LICENSE AGREEMENT

This Software License Agreement ('Agreement') is entered
into as of [DATE] by and between [LICENSOR] ('Licensor')
and [LICENSEE] ('Licensee')..."

[Immediately starts with properly formatted contract]

Output: 800 tokens
================================================================

Same quality output. 60% fewer tokens. That's cost savings.

The Tradeoffs

FINE-TUNING TRADEOFFS
================================================================

WHAT YOU GAIN:
- Better performance on your specific tasks
- More direct, relevant responses (fewer tokens)
- Consistent formatting and style
- Can embed company knowledge and terminology

WHAT YOU MIGHT LOSE:
- Some general capabilities
- Flexibility for unexpected tasks
- Money and time to create training data
- Ongoing maintenance as your needs evolve

================================================================
   Best for: Repeated, predictable tasks with clear requirements
   Not ideal for: General-purpose chatbots needing broad knowledge
================================================================

Real-World Example

A customer support AI for a software company.

General model:

• Gives accurate but generic answers
• Sometimes suggests features the product doesn't have
• Uses industry jargon the customers don't understand
• Average 1,500 tokens per response

Fine-tuned model:

• Knows every product feature exactly
• Uses the company's specific terminology
• Responds in the brand voice
• Average 600 tokens per response

The fine-tuned model costs 60% less per interaction AND provides better answers.

Combining Techniques

Here's where it gets interesting: these techniques can be combined.

TECHNIQUE COMBINATIONS
================================================================

EXAMPLE: Creating an efficient customer service AI

Step 1: Start with a large, capable model
        (500B parameters, very expensive)

Step 2: Distillation - Train a smaller student model
        (50B parameters, 10x cheaper)

Step 3: Fine-tuning - Specialize for customer support
        (50B parameters, 40% fewer tokens needed)

Step 4: Quantization - Reduce precision
        (50B params, 2x faster, runs on cheaper GPUs)

Step 5: Pruning - Remove unused connections
        (20B parameters, even faster)

RESULT:
- Original: $0.03 per response, 3 seconds
- Optimized: $0.002 per response, 0.4 seconds

That's 93% cost reduction and 7x speed improvement.

================================================================

This is how companies serve AI to millions of users affordably.

What This Means For You

If You're a Curious User

You're already benefiting from these techniques.

When you use GPT-4o mini or Claude Haiku instead of the flagship models (the most powerful, expensive models a company offers, like GPT-4o or Claude Opus), you're using distilled, optimized models. They're cheaper and faster precisely because of these techniques.

Practical tips:

1. Don't default to the biggest model. For most tasks, optimized smaller models work great.

2. Match the model to the task. Writing a quick email? Use the mini model. Complex analysis? Use the flagship.

3. Notice when quality drops. If a smaller model isn't cutting it for a specific task, that's useful information. Use it to decide when to upgrade.

If You're a Freelancer or Solopreneur

These techniques can dramatically reduce your AI costs.

If you're using AI APIs regularly, understanding model tiers helps you spend smarter.

Practical tips:

1. Start with smaller models. Use GPT-4o mini or Claude Haiku for first drafts and brainstorming.

2. Upgrade strategically. Only use flagship models for final polish or complex tasks.

3. Consider fine-tuning (eventually). If you use AI for the same type of task hundreds of times per month, a fine-tuned model could cut your costs significantly. This requires technical setup but pays off at scale.

4. Track your spending by task type. You might discover 80% of your tokens go to tasks that a mini model could handle.

Quick Reference: When to Use What

MODEL OPTIMIZATION DECISION GUIDE
================================================================

NEED                         TECHNIQUE
----                         ---------
Smaller file size            -> Quantization
Faster responses             -> Quantization + Pruning
Lower costs                  -> Distillation
Better at specific task      -> Fine-Tuning
All of the above             -> Combine them

----------------------------------------------------------------

USE CASE                     RECOMMENDED APPROACH
--------                     --------------------
General chatbot              Distilled model (e.g., GPT-4o mini)
Customer support             Fine-tuned + quantized
Mobile app                   Heavily pruned + quantized
High-stakes analysis         Full flagship model (no optimization)
High-volume, simple tasks    Maximum optimization

================================================================

Coming Up Next

Part 11: FinOps for AI (Governing Your AI Spending)

We've covered how AI costs work, why they grow, and how to optimize models. But how do you actually manage AI spending in practice?

In Part 11, we'll cover:

• What FinOps is and why it matters for AI
• Visibility: Knowing where your tokens go
• Governance: Preventing runaway costs
• Optimization: Right-sizing your AI usage
• Accountability: Making costs visible to stakeholders

These are the practices that separate organizations who control their AI spending from those who watch it spiral.

Your Homework for Part 11

Think about your AI usage:

1. Do you know exactly how much you spend on AI each month?
2. Could you break down that spending by task type or project?
3. Is anyone reviewing whether that spending makes sense?

If you answered "no" to any of these, you need FinOps.

See you in Part 11.

As always, thanks for reading!