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Understanding LLM Parameters

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Alright folks—this one’s been the question in my inbox lately:

“What does it actually mean when a model has 7 billion parameters? Or 70 billion? And why should I care?”

Let’s break it down in a way that works whether you write code for a living… or just want to sound knowledgeable amongst your friends.

What Do “Parameters” Mean in Large Language Models (LLMs)?

If you’ve heard people talk about modern AI, you’ve definitely heard phrases like:

  • “This is a 7B parameter model”

  • “That one is 175B parameters”

  • “More parameters = smarter AI”

Some of that is true. Some of it is marketing. And some of it is misunderstood—even by technical folks.

Let’s clear it up.

First: What Is a Parameter?

At its simplest, a parameter is a number the model learned during training.

That’s it.

More precisely:

  • Parameters are weights inside a neural network

  • They determine how strongly one concept influences another

  • They’re adjusted during training so the model can predict the next word correctly

If that sounds abstract, here’s a better analogy 👇

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SDLC in the Age of AI: When to Use Large LLMs vs. Small LLMs for Every Stage

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Large language models (LLMs) are reshaping how software systems are designed, built, and maintained. But not every task in the SDLC should default to the largest model you can find. Smaller language models (SLMs) — whether distilled LLMs or domain-specific language models — have unique advantages when applied to the right problems.

To make the best architectural decisions, teams must understand where in the SDLC large LLMs truly add value versus where small models shine.

In this context:

  • Large Language Models (LLMs) mean models with billions to trillions of parameters trained on broad corpora, offering deep understanding and generative capabilities.

  • Small Language Models (SLMs) are compact models optimized for specific domains or real-time inference on resource-limited hardware.

Why Model Size Matters in SDLC

At high level:

Property Large LLMs Small LLMs/SLMs
Model Scale Billions+ parameters Millions–hundreds of millions
Compute Needs High (cloud GPU/TPU) Low (edge/devices)
Generalization Broad, deep Narrow, domain-specific
Cost High Low
Inference Slower, richer output Fast, efficient

 Understanding these trade-offs helps tailor the SDLC process — from requirements to deployment — in a way that balances cost, complexity, and user expectations.

A Visual SDLC: LLM Size Meets Development Phase

Here’s a practical diagram that overlays SDLC phases with recommended LLM/SLM usage:

 

Small models dominate early specification parsing and edge inference — large models take over as complexity and context grow.

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