Ollama vs Llama.cpp: Complete Comparison Guide

Have you tried using ChatGPT or other AI tools? If you're curious about "what if I could run LLM on my own computer?", you've likely encountered 'Ollama' and 'Llama.cpp'. Both are tools that allow you to run AI models locally on your PC, but they have distinct characteristics and purposes.

This article provides a detailed comparison of the features, advantages, and disadvantages of both tools. After reading this guide, you'll understand the differences between Llama.cpp and Ollama, and be able to choose the most suitable solution for your needs.

What is Llama.cpp? - 'The Engine'

Llama.cpp is a foundational tool for running LLM locally. Simply put, Llama.cpp is a lightweight engine that runs LLM.

What is it?

Llama.cpp is a lightweight inference engine written in C/C++. It enables Meta's LLaMA and other LLM models to run efficiently on personal computers.

While most AI tools run in Python environments, running them directly on a PC can be heavy. Llama.cpp leverages C++ optimization to make even large AI models run smoothly. It can run LLM even in CPU-only environments without GPU.

What are the advantages?

Advantages:

• Fast Speed: Written in C++, it's optimized for speed. It's 13%~80% faster than Ollama. For example, Llama.cpp can generate 161 tokens per second, while Ollama produces 89 tokens, making Llama.cpp 1.8 times faster.

• Flexible Control: You can fine-tune all settings including memory management, thread count, and GPU allocation.

• Broad Hardware Support: Compatible with CPU, NVIDIA GPU, Apple Silicon (M1/M2/M3), and more.

Disadvantages:

• High Learning Curve: You need to set everything up yourself, which can be challenging for beginners.

• Complex Model Management: Difficult to manage as you need to find and download models manually.

• Lack of Convenience Features: No built-in model version control, update functions, or automatic optimization features.

How to Use Llama.cpp?

Here's how to use Llama.cpp:

Step 1: Install

# Clone from GitHub
git clone <https://github.com/ggerganov/llama.cpp>
cd llama.cpp

# Build (Mac/Linux)
make

# Or use CMake
cmake -B build
cmake --build build --config Release

Step 2: Download Model

• Download GGUF format models from Hugging Face (e.g., llama-2-7b.Q4_K_M.gguf)

Step 3: Run

# CLI execution
./llama-cli -m ./models/llama-2-7b.Q4_K_M.gguf -p "What is artificial intelligence?" -n 128

# API server mode
./llama-server -m ./models/llama-2-7b.Q4_K_M.gguf --port 8080

You need to use commands to control everything manually. The -m option specifies the model, -p is the prompt, and -n sets the number of tokens to generate.

Who Should Use It?

Llama.cpp is suitable for those who want detailed control over AI models. If you prefer using command-line interfaces (CLI) or need to optimize every aspect of performance, or if you need to "push speed to the limit" or "customize AI models," Llama.cpp is the right choice.

What is Ollama? - 'The User-Friendly Tool'

What is Ollama? While Llama.cpp is 'the engine,' Ollama is a tool that wraps this engine for easier use, essentially a 'user-friendly package'.

What is it?

Ollama is built on top of Llama.cpp. Think of it as a 'wrapper()' around Llama.cpp. It makes Llama.cpp's complex features easy for anyone to use.

It works like Docker. Just as Docker simplifies complex deployment with Dockerfile, Ollama uses 'Modelfile' to make LLM management simple. Just type ollama run llama3 and the model downloads and runs automatically.

What are the advantages?

Advantages:

• Easy to Use: Simple commands handle everything. No need to memorize complex options. Just run the model name.

• Convenient Model Management: Use simple commands like ollama list to check installed models, ollama run to execute them, and ollama rm to delete them. You can easily manage dozens of models.

• Automatic Updates: Models are automatically updated to the latest versions, eliminating manual downloads.

• Built-in REST API: Easily integrate LLM into your applications via REST API.

Disadvantages:

• Slightly Slower: Runs 13-80% slower than Llama.cpp. The abstraction layer adds overhead. While not noticeable for simple queries, the difference becomes apparent for large-scale processing.

• Less Flexibility: Cannot fine-tune settings as precisely as Llama.cpp. Some advanced features may be unavailable.

Who Should Use It?

Ollama is perfect when you want to focus on using LLM without worrying about technical details. If you need to "get started quickly," "manage multiple models comfortably," or "build API servers easily," Ollama is the answer.

Core Difference: Ollama and Llama.cpp 'Are Not Competing Tools'

Let's clarify an important point. "Llama.cpp vs Ollama, which is better?" is actually the wrong question.

Ollama is built on top of Llama.cpp. In other words, Ollama uses Llama.cpp as its engine. Ollama exists to make Llama.cpp easier to use.

The relationship is:

• Llama.cpp = Engine (focused on performance)

• Ollama = Tool (focused on usability)

Rather than asking "which is better?", you should ask "which approach suits my situation?"

Detailed Comparison? 5 Key Criteria

Here's a detailed comparison table. Check how they differ.

Which Should You Choose? - Recommendation by Situation

Here are recommendations based on actual use cases:

Choose Ollama if you...

Want to use LLM quickly and conveniently

Need to integrate with Python, JavaScript via API

Want to manage multiple models easily

Choose Llama.cpp if you...

Need to optimize performance through command-line interface (CLI)

Require detailed settings for AI research

Need maximum speed without API overhead

Key points:

Point 1: Start with Ollama. It only takes 30 seconds to install. Experience running LLM locally.

Point 2: If you need more speed after using Ollama, try Llama.cpp.

Point 3: If customization is more important than speed, Llama.cpp is the answer.

Choose the tool that matches your goals.

Conclusion:

Llama.cpp and Ollama are not competing tools but complementary solutions. To summarize:

• Llama.cpp is suitable when speed and customization are priorities. Perfect for developers and researchers.

• Ollama is suitable when ease of use and quick start are priorities. Perfect for beginners and API developers.

• You can use both simultaneously depending on your needs.

In the end, the right choice depends on your purpose and situation. If you want to start LLM quickly, Ollama is recommended. Once you've built a sufficient understanding through Ollama and need to expand your AI application or maximize performance, questions like "Should I optimize specific settings?" or "Do I need maximum speed?" will naturally lead you to Llama.cpp.

I hope this guide helps you successfully experience running LLM locally.