Local AI Fundamentals
What This Article Covers
- What local AI is and when it makes sense to use it.
- The difference between local and cloud-based AI.
- Key concepts like quantization, context length, and memory requirements.
- Links to all foundational articles.
Introduction
Local AI means running language models on your own computer or server. Privacy, independence from cloud providers, and full control over your data and workflows are the main reasons. If you’re getting started, you should understand what hardware you need, how to shrink models, and why context window matters.
Why Do You Need Local AI Fundamentals?
Without the basics, you’ll hit walls on seemingly simple problems. For example: you download a 13B model and wonder why it won’t run on your machine with 16 GB of RAM. Without foundational knowledge, you won’t realize you need quantization to compress the model down to 8 GB. Or you use a model with a small context window and can’t understand why it struggles with long documents.
Fundamentals also help you decide whether local AI makes sense for your use case. Short experiments often work fine in the cloud. But for data-sensitive applications or continuous use without API costs, local AI becomes worthwhile.
Local AI Explained
Local AI loads a language model onto your computer and runs it there. You need a runtime environment like Ollama or LM Studio to load the model and expose an API. The model lives in RAM or VRAM and calculates responses locally. No data leaves your machine.
The difference from cloud AI is straightforward: with cloud AI, you send your inputs to a provider’s server like OpenAI or Anthropic. With local AI, everything stays on your machine. It costs zero API dollars, but requires suitable hardware.
Who This Is For
- You want to run AI locally and need to know what your hardware can handle.
- You’re wondering if local AI fits your use case.
- You’re new to this and want to understand the concepts before installing Ollama.
- You don’t need deep prior knowledge. Technical terms are explained inline.
Key Terms
- LLM - Large Language Model, a powerful language model. Examples include Llama 3.1, Qwen 2.5, or Mistral.
- Quantization - Reducing model precision to save memory. Turns 16 GB models into 8 GB or smaller.
- Context length - The maximum input length a model can process at once. Measured in tokens.
- Ollama - Local runtime for language models. Loads models and provides an API.
- LM Studio - GUI application for local models with a user interface.
- Token - The smallest meaningful unit for a language model. One word is roughly 1.5 tokens.
- Inference - Running a model to generate responses. Training creates models; inference uses them.
Content and Articles
- What Is Local AI? - Definition, advantages, and typical use cases.
- Local AI vs. Cloud AI - When local AI makes sense and when cloud is better.
- Running LLMs Locally - First steps with Ollama, LM Studio, or Docker.
- Quantization - How large models fit on smaller hardware.
- Context Length - How much text a model can process at once.
- RAM and VRAM Requirements - What your machine needs for local models.
- AI Fundamentals Glossary - All key terms explained A to Z.
Common Pitfalls
- Model won’t fit in memory - Without quantization, large models won’t run on consumer hardware. Check memory requirements upfront.
- Context window is too small - A 4K-context model can’t handle long documents. For RAG, you need a model with a larger context window.
- Cloud costs vs. hardware costs - Local AI isn’t free. You pay in hardware and electricity. For occasional use, cloud might be cheaper.
- Not every model runs locally - Some models are built for cloud infrastructure. Check if a quantized version exists.
Further Reading
FAQ - Frequently Asked Questions
What is local AI?
Local AI means running language models on your own computer or server. Models live in RAM or VRAM and generate responses locally. No data leaves your machine. See What Is Local AI? for more.
What’s the difference between local AI and cloud AI?
With cloud AI, you send inputs to a provider’s server. With local AI, everything stays on your machine. Local AI has no API costs but requires suitable hardware. See Local AI vs. Cloud AI for details.
What is quantization?
Quantization reduces model precision to save memory. A 16 GB model becomes 8 GB or smaller. This lets you run larger models on smaller hardware. See Quantization for more.
What is context length?
Context length is the maximum input length a model can process at once, measured in tokens. An 8K-context model handles longer documents than a 4K-context model. See Context Length for details.
Is local AI worth it for beginners?
Yes. Tools like Ollama make it easy to get started. Larger models do require decent hardware though.
Do I need a graphics card for local AI?
Small models can run on CPU alone. For faster responses and larger models, a GPU with enough VRAM is recommended.
How much RAM do I need for local AI?
For 7B models, at least 16 GB of RAM, ideally 32 GB. Larger models like 70B need 64 GB or more. See RAM and VRAM Requirements for details.
What software do I need for local AI?
The simplest option is Ollama. It loads models and provides an API. LM Studio is a graphical alternative. See Local AI Software for an overview.
What is a token?
A token is the smallest meaningful unit for a language model. One word is roughly 1.5 tokens. Context length is measured in tokens.
What’s the difference between training and inference?
Training creates a model using large datasets. Inference runs a finished model to generate responses. Local AI uses inference.
Are local models as good as cloud models?
Large local models like Llama 3.1 70B come close to cloud models. Smaller 7B models are weaker but good enough for many tasks. The gap narrows as open-source models improve.
Can I run local AI with Docker?
Yes. Ollama, LM Studio, and many frameworks work in Docker. This is especially useful when you want to isolate multiple services.