Local AI
What This Article Covers
- What local AI is and when it makes sense to use it.
- The content available on BotServ in the local AI category.
- Links to fundamentals, software, models, RAG, and related topics.
Introduction
Local AI runs on your own computer or server. Privacy, control, and independence from cloud providers are the main benefits. This section takes you from the basics through software and models to RAG and local agents.
Why Do You Need Local AI?
If you use AI regularly, cloud provider API calls add up fast. With local AI, those recurring costs disappear. You pay once for hardware and then only for electricity. Add in privacy: sensitive data never leaves your machine.
Consider this scenario: you want to make internal documents searchable with RAG. On the cloud, you’d have to upload those documents to a provider. Locally, everything stays on your computer. For many companies, law firms, and individuals, this is a decisive advantage.
Local AI in a Nutshell
Local AI loads a language model onto your machine and runs it there. You need a runtime environment like Ollama or LM Studio to load the model and expose an API. The model sits in RAM or VRAM and computes responses locally. No data leaves your computer.
Who Should Read This
- You want to run AI locally and understand what your hardware can handle.
- You’re looking for software, models, or RAG solutions for self-hosting.
- You’re new to the topic and want to understand the concepts before installing Ollama.
- You don’t need deep prior knowledge. Technical terms are explained inline throughout the articles.
Content and Articles
- Fundamentals - What local AI is, quantization, context length, RAM and VRAM.
- Software - Ollama, LM Studio, and tools for local deployment.
- Models - How to find the right local models for your needs.
- Local RAG - RAG basics, chunking, embeddings, and vector databases.
Key Terms
- LLM - Large Language Model. Examples include Llama 3.1, Qwen 2.5, and Mistral.
- Ollama - Local runtime for language models. Loads models and provides an API.
- LM Studio - GUI application for local models with a user-friendly interface.
- Quantization - Reduces a model’s precision to save memory.
- RAG - Retrieval-Augmented Generation. Searches for relevant text passages in a database and provides them to the model as context.
- Vector Database - Stores embeddings for RAG search. Examples include Chroma and Qdrant.
Common Pitfalls
- Model doesn’t fit in memory - Without quantization, large models won’t run on consumer hardware. Check how much memory a model needs before downloading.
- Context window is too small - A model with 4K context 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 for hardware and electricity. For occasional use, the cloud might be cheaper.
Further Reading
FAQ
What is local AI?
Local AI means running language models on your own computer or server. Models reside in RAM or VRAM and compute responses locally. No data leaves your machine. Learn more in Local AI Fundamentals.
What’s the difference between local AI and cloud AI?
With cloud AI, you send requests to a provider’s servers. With local AI, everything stays on your machine. Local AI has no API costs but requires suitable hardware.
What software do I need for local AI?
Ollama is the simplest solution. It loads models and provides an API. LM Studio is a graphical alternative. Learn more in the Local AI Software overview.
What is quantization?
Quantization reduces a model’s precision to save memory. A 16 GB model becomes 8 GB or smaller. This makes larger models run on less powerful hardware.
Do I need a GPU for local AI?
A CPU is often enough for small models. For faster responses and larger models, a GPU with sufficient VRAM is recommended.
How much RAM do I need for local AI?
For 7B models, at least 16 GB RAM, preferably 32 GB. Larger models like 70B require 64 GB or more.
What is RAG?
RAG stands for Retrieval-Augmented Generation. The model searches for relevant text passages in a database and uses them as context for the answer. Learn more in the Local RAG overview.
What is a vector database?
A vector database stores embeddings, which are numerical representations of text. It’s used for RAG search. Examples include Chroma and Qdrant.
Are local models as good as cloud models?
Large local models like Llama 3.1 70B come close to cloud models. Small 7B models are weaker but sufficient 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 if you want to run multiple services in isolation.