Local LLM

Run AI models locally with Ollama, LM Studio, and open-source LLMs — privacy-first AI development

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Running LLMs locally gives you privacy, zero API costs, and offline capability. The tooling has matured to the point where a one-command install gets you a ChatGPT-quality experience on consumer hardware.

Tool Comparison

Tool	Best for	UI	API	GPU support
Ollama	Developers, scripting, Docker	CLI	✅ OpenAI-compatible	NVIDIA, AMD, Apple
LM Studio	Beginners, GUI, model browsing	✅ Desktop	✅ Local server	NVIDIA, Apple Silicon
GPT4All	Offline-first, no telemetry	✅ Desktop	✅	CPU + NVIDIA
Jan AI	Privacy-focused desktop chat	✅ Desktop	✅	NVIDIA, Apple
llama.cpp	Maximum control, custom builds	CLI	✅ Server mode	All backends
AnythingLLM	Teams, document chat, RAG	✅ Web UI	✅	Via Ollama

Hardware Requirements

Model size	RAM needed	GPU option	Quality
3B–4B (Q4)	4GB RAM	CPU OK	Good for simple tasks
7B–8B (Q4)	8GB RAM	6GB VRAM	General purpose
13B (Q4)	16GB RAM	8GB VRAM	Strong reasoning
70B (Q4)	48GB RAM	24GB VRAM	Near GPT-4 level

Quick Start — Three Options

# Option 1: Ollama (developers, API-first)
curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3.3

# Option 2: LM Studio (beginners, GUI)
# Download from lmstudio.ai → search models → one-click download

# Option 3: llama.cpp (maximum control)
git clone https://github.com/ggml-org/llama.cpp
make GGML_CUDA=1  # NVIDIA GPU
./llama-server -m models/llama-3.3-8b-q4_k_m.gguf --port 8080

Model Selection Guide

Model	Strengths	Quantization	Tool
Llama 3.3 8B	General purpose, fast	Q4_K_M	Ollama, LM Studio
DeepSeek R1 7B	Reasoning, math	Q4_K_M	Ollama
Qwen 2.5-Coder 7B	Code generation	Q4_K_M	Ollama, LM Studio
Mistral 7B	Fast, instruction following	Q4_K_M	Ollama
nomic-embed-text	Embeddings only	—	Ollama

Learning Path

Install your tool — Ollama for API access, LM Studio for GUI
Pick the right model — match task to model strengths
Understand quantization — Q4_K_M is the default sweet spot
Configure GPU offloading — maximize VRAM usage
Connect to your app — OpenAI-compatible API, LangChain, or direct HTTP
RAG with local LLMs — combine with Ollama embeddings + pgvector

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