Ollama vs llama.cpp – Choosing Your Local LLM Engine

Run ChatGPT-like models locally—without sending your data to the cloud. No API keys. No rate limits. No monthly bills. Just you, your machine, and an AI that runs entirely on your hardware.

But here’s the question every newcomer faces: Which tool should you use?

Two names dominate the local LLM space: Ollama and llama.cpp. Both let you run models like Llama 3, Mistral, and Gemma on your own machine. But they take fundamentally different approaches.

Let’s break down what each does, who they’re for, and how to choose.

What Each Tool Actually Is

Ollama: The “It Just Works” Solution

Ollama is a user-friendly wrapper that makes running local LLMs feel like using any other desktop app. Download it, type one command, and you’re chatting with an AI model.

Think of it as the Docker of LLMs — it handles model downloads, version management, and API endpoints so you don’t have to.

What you get:

One-line installation (macOS, Windows, Linux)
Built-in model library (Llama 3, Mistral, Gemma, Phi, CodeLlama, etc.)
REST API out of the box
Official Python and JavaScript client libraries
Automatic hardware detection and optimization

Example usage:

# Install Ollama, then run:
ollama run llama3

# That's it. You're now chatting with Llama 3.

llama.cpp: The Engine Builder’s Toolkit

llama.cpp is a raw, MIT-licensed C/C++ inference engine. It’s what the hackers and builders use when they want complete control over how models run.

Think of it as the Linux kernel of LLM inference — powerful, flexible, but you’re responsible for the plumbing.

What you get:

Pure C/C++ implementation (no dependencies)
Maximum performance optimization
Full control over memory, threading, quantization
Build from source, customize everything
Direct integration into your own applications

Example usage:

# Build from source
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
make

# Download a model, then run:
./main -m ./models/llama-3-8b.Q4_K_M.gguf -p "Hello, how are you?"

The Mental Model: Engine vs Car

Here’s the simplest way to understand the difference:

llama.cpp is the engine.
Ollama is a car with that engine already installed.

With Ollama, you turn the key and drive. With llama.cpp, you’re in the garage with a wrench, tuning the fuel injection and customizing the exhaust.

Both will get you where you’re going. The question is: do you want to drive, or do you want to build?

Comparison Table: The Key Differences

Aspect	Ollama	llama.cpp
Best for	Beginners, quick starts, app developers	Tinkerers, performance optimization, custom integrations
Setup complexity	One download, done	Build from source, configure options
Model management	Built-in (pull, list, push models)	Manual (download GGUF files yourself)
API surface	REST API + Python/JS libraries	Direct C/C++ integration
Customization	Configuration via Modelfile	Compile-time and runtime flags
Performance tuning	Automatic hardware detection	Manual control over threads, memory, GPU offload
Learning curve	5 minutes	1-2 hours (first time)
Use case	“I want it to work now”	“I want to understand how it works”

When to Choose Ollama

Pick Ollama if you check any of these boxes:

1. You Want to Run a Model in Under 5 Minutes

# Literally this simple:
brew install ollama        # macOS
ollama run llama3          # Done. You're chatting.

2. You’re Building an App and Need an API

Ollama exposes a full REST API:

POST http://localhost:11434/api/generate
{
  "model": "llama3",
  "prompt": "Why is the sky blue?"
}

Plus official client libraries:

# Python
pip install ollama

import ollama
response = ollama.chat(model='llama3', messages=[
  {'role': 'user', 'content': 'Why is the sky blue?'}
])
print(response['message']['content'])

// JavaScript
npm install ollama

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'llama3',
  messages: [{ role: 'user', content: 'Why is the sky blue?' }]
})
console.log(response.message.content)

3. You Want Built-in Model Management

# List installed models
ollama list

# Download a new model
ollama pull mistral

# Remove a model
ollama rm codellama

# Create a custom model with a Modelfile
ollama create mymodel -f ./Modelfile

4. You’re on macOS or Windows and Don’t Want to Think About Compilers

Ollama detects your hardware (Apple Silicon, NVIDIA GPU, CPU-only) and optimizes automatically.

When to Choose llama.cpp

Pick llama.cpp if you check any of these boxes:

1. You Want Maximum Performance Control

# Fine-tune every parameter:
./main -m model.gguf \
  -t 8                    # Use 8 threads
  -ngl 40                 # Offload 40 layers to GPU
  -c 4096                 # 4096 context window
  --temp 0.7              # Temperature for sampling
  --top-p 0.9             # Top-p sampling
  --mlock                 # Lock memory, prevent swapping
  --no-mmap               # Don't use memory mapping
  -b 512                  # Batch size

2. You’re Embedding LLMs Into a Custom Application

With llama.cpp, you’re not limited to HTTP APIs. You can:

Link directly as a C/C++ library
Use Python bindings (llama-cpp-python)
Build into embedded systems, mobile apps, or edge devices
Integrate into game engines, robotics, or IoT

# Python bindings example
pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="./llama-3-8b.Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=40  # GPU offload
)

output = llm("Q: Why is the sky blue? A:", max_tokens=100)
print(output)

3. You Want to Understand How LLM Inference Works

llama.cpp’s codebase is educational. You’ll learn about:

Quantization (Q4_K_M, Q5_K_S, Q8_0, etc.)
Tokenization and context windows
KV-cache management
Memory-efficient inference
GPU acceleration (CUDA, Metal, ROCm, Vulkan)

4. You Need Minimal Dependencies

llama.cpp compiles to a single binary. No runtime dependencies. Perfect for:

Air-gapped environments
Containerized deployments
Resource-constrained devices
CI/CD pipelines

Quick Decision Guide

Choose Ollama if:

✅ You’re new to local LLMs
✅ You want a REST API for your app
✅ You prefer official client libraries
✅ You want model management built-in
✅ You’re on macOS/Windows and want it “to just work”

Choose llama.cpp if:

✅ You’re comfortable with C/C++ or building from source
✅ You need fine-grained performance tuning
✅ You’re embedding into a custom application
✅ You want to learn how LLM inference actually works
✅ You need minimal dependencies for deployment

Can You Use Both?

Absolutely. In fact, here’s a common pattern:

Start with Ollama for quick prototyping and experimentation
Switch to llama.cpp when you need performance optimization or custom integration

Or use them together:

Ollama for your chatbot API
llama.cpp for batch processing or fine-tuned inference

What’s Next?

In the next post, we’ll do a hands-on installation guide:

How to install Ollama on macOS, Windows, and Linux
How to build llama.cpp from source
Running your first model on each
Benchmarking performance on your hardware

Closing Thought

The local LLM revolution isn’t about choosing the “best” tool. It’s about choosing the right tool for your situation. Ollama gets you running in minutes. llama.cpp gives you control for years. Both are free, open-source, and evolving fast.

Start with whichever feels less intimidating. You can always switch later — or use both.

This is Part 1 of the LLM Tools series under AI Foundations.

Next: Installing and Running Your First Local LLM (coming soon)