Local LLMs: Running AI Models on Your Own Hardware

Why Run LLMs Locally?

While cloud APIs are convenient, there are compelling reasons to run AI models on your own hardware: privacy, cost control, offline capability, and customization freedom.

Benefits and Trade-offs

Why Local?

Trade-offs

Hardware Requirements

By Model Size

GPU Options

Apple Silicon

CPU-Only (Slower)

For models with no GPU:

• 32GB+ RAM recommended
• llama.cpp CPU mode
• Expect 5-20 tokens/second
• Still usable for many tasks

Software Tools

Ollama (Recommended Start)

# Install
curl https://ollama.ai/install.sh | sh

# Run a model
ollama run llama3.1

# API server
ollama serve
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1",
  "prompt": "Hello, world!"
}'

Pros: Easiest to use, just works Cons: Less configuration options

llama.cpp

# Clone and build
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make LLAMA_CUDA=1  # For NVIDIA

# Run model
./llama-server -m models/llama-3.1-8b-q4.gguf

# Use the API
curl http://localhost:8080/completion -d '{"prompt": "Hello"}'

Pros: Maximum performance, highly optimized Cons: More technical setup

LM Studio

• GUI application
• Download models easily
• One-click inference
• OpenAI-compatible API
• Windows, Mac, Linux

Best for: Non-technical users

vLLM

pip install vllm
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-8B-Instruct

Pros: Production-grade, high throughput Cons: Requires more setup

Model Selection

Best Open Models for Local

Quantization Levels

Where to Get Models

Use Cases

When Local Makes Sense

When Cloud is Better

Performance Optimization

Tips

Expected Performance

Getting Started

Quick Start Path

Integration Options

Future Trends

What's Coming

1. More efficient models: Same quality, smaller size
2. Better Apple Silicon: Optimized for M-series
3. NPU acceleration: Windows AI PC support
4. On-device fine-tuning: Personalization
5. Hardware improvements: Consumer AI chips

"Local LLMs are becoming increasingly viable. With the right hardware and a quality open-source model, you can have a capable AI assistant that's completely private and costs nothing to run."