What Is AI, What Is an LLM, and Why They Matter Now

A Short, Practical View of AI

“Artificial Intelligence” covers many ideas, from search heuristics to deep learning. In day‑to‑day engineering conversations, when people say AI today they usually mean systems that:

Learn patterns from data rather than only following hand‑written rules,
Make predictions or decisions under uncertainty, and
Improve over time as they see more data or feedback.

Typical production uses are concrete and measurable: ranking search results, approving or flagging transactions, forecasting demand, scoring leads, or classifying images and sensor signals. Under the hood, most of these workloads are powered by machine learning (ML) models – parameterized functions trained to approximate a mapping from inputs to outputs.

LLMs sit on top of this ML foundation, but they feel different enough that it is worth treating them separately.

What Is a Large Language Model (LLM)?

A large language model (LLM) is a neural network trained on massive text (and often code) corpora to perform one core task: predict the next token given the previous tokens. A token is just a small chunk of text – usually a few characters or part of a word.

The surprising part is what emerges from doing this at scale:

The model internalizes syntax and semantics of natural languages and programming languages.
It can answer questions, summarize documents, translate, write code, and reason over structured and unstructured inputs – all through the same interface: text in, text out.

From an engineer’s point of view, an LLM at inference time is a pure function exposed as an API:

You send a prompt and configuration (temperature, max tokens, tools, etc.).
The serving stack handles routing, batching, rate limiting, logging, and safety.
You receive a completion plus structured metadata (usage, latency, tool calls) that you can plug back into your system.

That simple abstraction hides a lot of complexity in training, infrastructure, and safety layers – but it’s the abstraction most application and platform teams build on.

Concrete Examples of Modern LLMs

To make this less abstract, here are a few well‑known LLM families and where their official information lives. This is not an endorsement of any provider, just a map of the landscape a technical reader will repeatedly encounter:

GPT‑4‑class models (OpenAI) – Developed by OpenAI, accessible via the OpenAI API and various Copilot experiences. Official docs: https://platform.openai.com/docs.
Claude 3 family (Anthropic) – Developed by Anthropic, focused heavily on steerability and safety tooling. Official docs: https://docs.anthropic.com/.
Gemini models (Google DeepMind) – Google’s multimodal LLMs with tight integration into Google Cloud. Official docs: https://ai.google.dev/.
Llama 3 family (Meta) – Open‑weight models from Meta, widely used as a base for fine‑tuning and self‑hosted deployments. Model overview: https://ai.meta.com/llama/.
Mistral models (Mistral AI) – A set of strong open‑weight and hosted models from Mistral. Overview and docs: https://mistral.ai/.

Each of these families has slightly different strengths, licensing models, and deployment options (fully managed APIs, cloud‑hosted, or on‑prem / self‑hosted). For an architect, understanding those trade‑offs is as important as understanding the underlying math.

Why LLMs Feel Different from Previous AI

We have had production ML systems for many years, so why is this wave different? Three properties matter in practice:

General‑purpose behavior – The same base model can support dozens of tasks using only prompting and a small amount of fine‑tuning or adapters. You no longer need a separate classifier or ranker for every narrow problem.
Language‑native interfaces – The primary UX surface is natural language. Instead of building a bespoke UI for every workflow, you can expose capabilities through a chat, copilot, or command bar that sits next to where users already work.
Tool and data composition – Modern LLMs can decide when to call tools (APIs, workflows, queries) and how to integrate those results into answers. That shifts complexity from UI wiring to well‑designed tool and data contracts.

This is why LLMs show up not only as “chatbots” but as reasoning engines inside products – inside IDEs, productivity suites, analytics tools, and operational consoles.

Where AI and LLMs Sit in a Modern Stack

It’s useful to place LLMs explicitly in your architecture instead of viewing them as magic. A common mental model is a set of layers:

Systems of Record – CRMs, EMRs, ERPs, line‑of‑business apps, and their databases.
Knowledge & Context Layer – docs, design decisions, tickets, logs, runbooks, code, wikis, knowledge graphs.
Retrieval & Governance Layer – search indices, vector stores, ranking, sensitivity labels, access control, and data quality.
LLM & Orchestration Layer – the model(s) plus orchestration: routing, tool calling, safety policies, evaluations.
Experience Layer – end‑user surfaces: chat UIs, copilots in M365 or IDEs, portals, mobile apps, CLIs.

The LLMs live primarily in layer 4, but their usefulness is bounded by layers 2 and 3. Poor retrieval and weak governance will heavily limit what any model can safely and reliably do for your users.

Experience Layer
	└── Chat UIs, IDE copilots, dashboards

LLM & Orchestration
	└── Routing, tool calling, safety, evaluation

Retrieval & Governance
	└── Search, vector store, ranking, ACLs, labels

Knowledge & Context
	└── Docs, tickets, code, logs, runbooks, wikis

Systems of Record
	└── CRMs, EMRs, ERPs, line-of-business apps

Hardware, Energy, and Infrastructure – the Unseen Constraints

LLMs depend on a very real physical footprint:

Accelerators – GPUs and specialized chips provide the parallelism required for training and inference.
Data center design – power, cooling, network, and storage architectures must support high, bursty workloads.
Energy consumption – both training and inference have non‑trivial energy costs, which translate into operational cost and environmental impact.

At a high level, two numbers shape how hard it is to run a model:

Parameter count – how many learned weights the model has. This is a rough proxy for model capacity and memory requirements.
Context length and batch size – how many tokens you process at once, which affects both memory and compute.

Very roughly (orders of magnitude, not exact sizing):

Models in the 1–8 billion parameter range can often run on a modern laptop or desktop with a single consumer GPU and aggressive quantization.
Models in the 10–70 billion parameter range usually need one or more data‑center‑class GPUs with large memory, even when quantized.
The very largest foundation models require many GPUs in parallel, along with high‑bandwidth interconnects and careful sharding.

This is driving several trends that architects should keep in mind:

Moving from “one huge model for everything” toward model portfolios and routing: small, fast models for simple tasks, larger models only when needed.
Aggressive efficiency work (distillation, quantization, low‑rank adaptation, better batching) to reduce latency and cost.
More explicit capacity planning and placement decisions, especially in multi‑region or hybrid environments.

In other words, modern AI is not only an algorithm and data problem; it is a systems engineering, hardware, and energy problem as well.

Why Most Laptops Cannot Run Frontier‑Scale LLMs

Given the parameter and context sizes above, most laptops face three hard limits:

GPU memory – many consumer GPUs have 4–16 GB of VRAM. That is enough for smaller models but not for tens of billions of parameters plus activations, even with quantization.
Sustained power and thermals – running a large model at full load for long periods quickly hits thermal and power limits on thin‑and‑light devices.
Storage and bandwidth – model weights for very large models can be tens or hundreds of gigabytes. Loading and updating those locally is slow and storage‑heavy.

Because of this, most teams treat laptops as clients and orchestration surfaces, not as primary inference hosts for the biggest models. Instead, they:

Run small or medium‑size models locally for quick, private tasks.
Call out to cloud or data center deployments when they need the capacity of larger models.

Examples of models that are often practical on a single modern machine (with enough RAM, disk, and optionally a consumer GPU) include:

Open‑weight models in the 1–8B parameter range from projects such as Llama and Mistral.
Purpose‑built small language models designed for edge and on‑device scenarios.

Exact sizing depends on hardware, quantization, and tooling, but the pattern is stable: use smaller, efficient models locally and reach for larger hosted models only when their extra capability is truly needed.

Why This Matters to Technical Readers

For engineers and architects, LLMs change the questions you ask when designing systems:

How do we expose capabilities as tools or APIs that an LLM can safely compose?
How do we represent organizational knowledge so retrieval and grounding work well?
How do we build observability and evaluation around AI behaviors, not just around microservices?

If you are responsible for a platform, pipeline, or product, this is no longer optional background knowledge – it is part of core system design.