Local LLM 101: The Full Article Series

Here is the full Local LLM 101 series in order from the last few weeks.

Read it straight through if you are new.

Use it as a reference if you already know some pieces.

Hopefully you find some of these helpful.

00 - Introduction to Local LLMs

Start here if you are new to the local AI world.

This article explains what local LLMs are, why they matter, and why the best way to learn them is not by chasing model names, but by understanding the system underneath.

You will learn:

• what a local LLM is

• how local AI differs from cloud AI

• why local models matter for privacy, control, offline use, and experimentation

• the beginner mental model for the whole series

• why local LLMs are best understood as systems, not magic chat boxes

Read it here: 00 - Introduction to Local LLMs

Neo@NeoAIForecast·May 12 Article00 - Introduction to Local LLMsMost people use AI through a chat box. You type a message > The model answers > It feels almost instant. But under the surface, something much more interesting is happening. Your text gets broken into…22273.9K

01 - Inference and Sequences

This is the heartbeat of every LLM.

An LLM does not write a full answer all at once. It predicts the next token, appends that token to the sequence, then predicts again.

That repeated process is inference.

You will learn:

• what inference means

• why LLMs work with sequences

• how prompts become generated output

• why generation happens one token at a time

• why output length affects speed

• why local hardware matters during generation

Read it here: 01 - Inference and Sequences

Neo@NeoAIForecast·May 13 Article01 - Inference and SequencesPrevious Article 00 - Introduction to Local LLMs Most people think an LLM “writes an answer.” That is not quite right. A language model does something simpler, stranger, and more powerful: It…12122.3K

02 - Tokens, Tokenizers, and Context Windows

LLMs do not read text exactly like humans do.

They read tokens.

A token can be a word, part of a word, punctuation, whitespace, code fragment, or special marker. The tokenizer converts your text into token IDs, and the context window defines how many tokens the model can actively use.

You will learn:

• what tokens are

• why tokens are not always words

• what tokenizers do

• why the same text can tokenize differently across models

• what a context window is

• why long prompts slow down local models

• why models seem to “forget” older information

Read it here: 02 - Tokens, Tokenizers, and Context Windows

Neo@NeoAIForecast·May 14 Article02 - Tokens, Tokenizers, and Context WindowsPrevious Article 01 - Inference and Sequences Most think an LLM reads words. It does not. Before a local model can answer you, your text gets chopped into smaller pieces called tokens, converted into…13303K

03 - Weights, Parameters, and What the Model Learned

When people say a model has 7B, 14B, 70B, or 405B parameters, what does that actually mean?

This article explains what weights and parameters are without pretending they are simple facts in a database.

You will learn:

• what parameters are

• what weights do inside a model

• how training adjusts weights

• why model knowledge is stored as statistical patterns

• why bigger models can help, but do not guarantee better outputs

• why local model size affects memory, speed, and capability

Read it here: 03 - Weights, Parameters, and What the Model Learned

Neo@NeoAIForecast·May 15 Article03 - Weights, Parameters, and What the Model LearnedPrevious Article - 02 - Tokens, Tokenizers, and Context Windows A language model is not a database of facts. It does not have a neat little table that says: Paris = capital of France Python =…3231K

04 - What a Model Actually Includes

A model is not always just one file.

Depending on the format and runtime, a usable local model may include weights, architecture config, tokenizer files, chat templates, generation settings, special tokens, metadata, licenses, and format-specific packaging.

You will learn:

• what model architecture means

• why weights are only one part of the model package

• why tokenizer files matter

• what config files describe

• what chat templates do

• why licenses matter

• how formats like GGUF and safetensors fit into the picture

Read it here: 04 - What a Model Actually Includes

Neo@NeoAIForecast·May 16 Article04 - What a Model Actually IncludesPrevious Article - 03 - Weights, Parameters, and What the Model Learned When beginners first download a local LLM, they usually focus on one thing: The model size. 7B.
13B.
34B.
70B.
That…1183K

05 - Generation, Softmax, Greedy, and Sampling

Why can the same prompt produce different answers?

Because the model does not directly “choose words.” It produces scores for possible next tokens. Those scores become probabilities, and decoding settings decide what token gets selected.

You will learn:

• what logits are at a high level

• how softmax turns scores into probabilities

• what greedy decoding does

• why sampling creates variation

• how temperature changes randomness

• how top-k and top-p shape token choices

• why generation settings affect style, not the model’s underlying knowledge

Read it here: 05 - Generation, Softmax, Greedy, and Sampling

Neo@NeoAIForecast·May 17 Article05 - Generation, Softmax, Greedy, and SamplingPrevious Article - 04 - What a Model Actually Includes A LLM does not write an answer. A local LLM generates text one token at a time by repeatedly asking: “Given everything so far, what token should…171K

06 - KV Cache and Session Memory

KV cache is one of the most misunderstood local LLM concepts.

It helps the model continue generation efficiently by storing ntermediate attention information from previous tokens.

But it is not long-term memory.

You will learn:

• what the KV cache stores

• why it makes generation faster

• how it relates to prior tokens in the active context

• why KV cache is not learned knowledge

• why chat history, context, cache, and memory are different things

• why models cannot reliably use information outside their active context unless another system provides it

Read it here: 06 - KV Cache and Session Memory

Neo@NeoAIForecast·May 18 Article06 - KV Cache and Session MemoryPrevious Article - 05 - Generation, Softmax, Greedy, and Sampling A local LLM can continue a conversation because the runtime keeps the active context available, and because the KV cache lets the…1111K

07 - Transformers: The Core Engine

Most modern LLMs are built on the transformer architecture.

This article explains the transformer at a high level: how it processes token sequences, transforms representations through layers, and uses attention to let tokens influence each other.

You will learn:

• why transformers matter

• how token representations move through layers

• what attention does conceptually

• why transformers scale well with data and compute

• why they replaced many older sequence-modeling approaches

• how transformers power modern local LLMs

Read it here: 07 - Transformers: The Core Engine

Neo@NeoAIForecast·May 19 Article07 - Transformers: The Core EnginePrevious Article - 06 - KV Cache and Session Memory LLMs are not just magic text boxes. Under the surface, modern language models are powered by a specific kind of neural network architecture: the…1121.5K

08 - Transformer Layers and Self-Attention

Self-attention is one of the key ideas behind modern LLMs.

It lets each token look at other tokens in the sequence and decide which relationships matter.

That is how a model can connect pronouns to names, functions to variables, questions to earlier context, and instructions to the answer it is generating.

You will learn:

• what token representations are

• how self-attention lets tokens relate to each other

• why attention weights matter

• how layers refine representations

• what multi-head attention does conceptually

• why stacked layers build richer understanding

Read it here: 08 - Transformer Layers and Self-Attention

Neo@NeoAIForecast·May 20 Article08 - Transformer Layers and Self-AttentionMost people hear “attention” and imagine an LLM choosing what to focus on like a human.

That is close enough to be useful, but not precise enough to understand what is actually happening.

In a…21112.7K

09 - From Theory to Running a Local Model

This article connects the whole series to real local inference.

When you run a GGUF model through llama.cpp, Ollama, LM Studio, or another runtime, all the pieces from the previous articles come together.

You will learn:

• how a runtime loads model weights and config

• how chat templates format your message

• how tokenizers turn text into token IDs

• how the context window sets the active workspace

• how inference predicts one token at a time

• how sampling selects output tokens

• how KV cache speeds continuation

• why hardware determines practical speed and memory limits

• where GGUF, Ollama, LM Studio, and llama.cpp fit

Read it here: 09 - From Theory to Running a Local Model

Neo@NeoAIForecast·May 21 Article09 - From Theory to Running a Local ModelA local LLM does not “wake up” and start chatting.

When you run one, a whole chain of pieces snaps together: model files, tokenizer, chat template, context window, inference runtime, sampling…271K

Follow along for the next series as we go even deeper into the world of Local LLMs.