Run AI Locally

Here's the thing - running LLMs locally gives us complete control over our AI experience - privacy, no subscription costs, and offline functionality. In 2026, local AI is no longer just for techies; tools like Ollama and LM Studio make it accessible to everyone.

Benefits of Local AI

• 100% Privacy - Our prompts never leave our machine
• No Subscriptions - Free to use after setup
• Offline Access - Works without internet
• No Rate Limits - Use as much as we want
• Full Control - Customize models, parameters, everything
• No Censorship - Many local models are less restricted

Who Benefits from Local AI?

• Privacy-conscious developers - Keep sensitive data local
• Builders - Create apps without API costs
• Researchers - Experiment without limits
• Remote workers - Use AI anywhere, even offline
• Budget-minded teams - Eliminate ongoing subscriptions

In my view, Ollama is the most popular tool for running LLMs locally, especially among developers. It's command-line based but incredibly simple - exactly how we like it.

Installation

macOS / Linux

# One-line install
curl -fsSL https://ollama.com/install.sh | sh

Windows

Download the installer from ollama.com/download

Running Your First Model

# Download and run Llama 3.2 (8B)
ollama run llama3.2

# Or run DeepSeek Coder for programming
ollama run deepseek-coder:6.7b

# Or run Mistral for general tasks
ollama run mistral

Popular Models for Ollama

• llama3.2 - Meta's latest, great all-rounder (8B/70B)
• deepseek-coder - Excellent for coding (6.7B/33B)
• mistral - Fast and capable (7B)
• codellama - Code-focused Llama variant
• gemma2 - Google's efficient model
• phi3 - Microsoft's small but capable model

Using Ollama with Our Apps

# Python example with OpenAI-compatible API
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # Can be any string
)

response = client.chat.completions.create(
    model="llama3.2",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

Useful Ollama Commands

# List downloaded models
ollama list

# Pull a model without running it
ollama pull llama3.2

# Remove a model
ollama rm llama3.2

# See model info
ollama show llama3.2

# Run as a server
ollama serve

LM Studio provides the most polished graphical interface for running local LLMs. Here's the thing - if we want to avoid the command line, this is the best choice.

Installation

1. Go to lmstudio.ai
2. Download for your operating system
3. Install like any normal application
4. Launch and start exploring models

Downloading Models in LM Studio

1. Click the "Search" tab (magnifying glass icon)
2. Search for a model (e.g., "llama", "deepseek", "mistral")
3. Choose a quantization level based on your RAM
4. Click "Download"

Understanding Quantization

Models come in different sizes (quantization levels):

• Q4_K_M - Best balance of size/quality (recommended)
• Q5_K_M - Slightly better quality, larger
• Q8_0 - Near-original quality, much larger
• FP16/FP32 - Full precision, very large

RAM Requirements

Features

• Chat Interface - Beautiful UI for conversations
• Model Library - Browse and download models easily
• Local Server - Run as OpenAI-compatible API
• Preset Management - Save and share configurations
• JIT Loading - Models load on-demand

Running as a Server

1. Go to the "Local Server" tab
2. Select our model
3. Click "Start Server"
4. Use the API at http://localhost:1234/v1

Let me share the top models for running locally, ranked by use case based on my testing:

General Purpose

• Llama 3.2 (8B/70B) - Meta's latest, excellent all-around
• Mistral (7B) - Fast, capable, great for chat
• Gemma 2 (9B/27B) - Google's efficient model

Coding

• DeepSeek Coder (6.7B/33B) - Excellent code generation
• CodeLlama (7B/13B/34B) - Meta's code-focused model
• Qwen2.5-Coder - Strong multilingual coding

Creative Writing

• Llama 3.2 - Good creative output
• Mistral - Natural, flowing text
• Mixtral (8x7B) - Mixture of experts, very capable

Reasoning & Math

• DeepSeek R1 (distilled versions) - Chain-of-thought reasoning
• Qwen2.5-Math - Specialized for math
• Llama 3.2 - Good general reasoning

Small & Fast (8GB RAM)

• Phi-3 Mini (3.8B) - Microsoft's efficient model
• Gemma 2 (2B) - Google's tiny model
• Llama 3.2 (3B) - Meta's small model

Model Comparison

Let me share some practical ways we use local AI in our daily work:

1. Private Document Analysis

Analyze sensitive documents without uploading them:

• Legal documents
• Medical records
• Financial statements
• Proprietary business data

2. Offline Development

Build AI features without internet dependency - this is huge for us:

• Code completion
• Code review
• Documentation generation
• Bug hunting

3. Creative Writing

Write without censorship or data collection:

• Fiction writing
• Brainstorming
• Personal journaling
• Content creation

4. Learning & Experimentation

• Learn AI/ML concepts hands-on
• Fine-tune models on custom data
• Experiment with prompts without limits
• Build prototypes quickly

5. Home Automation

• Voice assistants that stay local
• Smart home integrations
• Personal AI butler

Example: Private Code Review

# Start Ollama with DeepSeek Coder
ollama run deepseek-coder:6.7b

# In the chat, paste our code:
> Review this code for security issues and suggest improvements:
> [paste our proprietary code]

# Our code never leaves our machine!

What hardware do we need for local AI? Here's a realistic guide based on my testing:

Minimum (Basic Usage)

• RAM: 8GB
• GPU: Not required (CPU works)
• Storage: 10GB free space
• Models: 7B parameter models (Q4)

Recommended (Good Experience)

• RAM: 16GB
• GPU: 8GB VRAM (RTX 3060/4060)
• Storage: 50GB SSD
• Models: Up to 13B models

Optimal (Best Performance)

• RAM: 32GB+
• GPU: 24GB+ VRAM (RTX 4090 / 5090)
• Storage: 100GB+ NVMe SSD
• Models: 33B-70B models

Apple Silicon (Mac)

M1/M2/M3/M4 Macs are excellent for local AI due to unified memory:

• M1/M2 (8GB) - 7B models work well
• M1/M2 Pro (16GB) - 13B models comfortable
• M1/M2 Max (32GB+) - 33B models possible
• M3/M4 Ultra (64GB+) - 70B models feasible

GPU vs CPU Performance