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How to Use llama.cpp to Run LLaMA Models Locally

Learn how to run LLaMA models locally using `llama.cpp`. Follow our step-by-step guide to harness the full potential of `llama.cpp` in your projects.

Large language models (LLMs) like Meta’s LLaMA have revolutionized natural language processing. However, not everyone wants to depend on cloud-based APIs to run them. That’s where llama.cpp comes in—a lightweight, open-source solution that lets us run LLaMA models locally, even on modest hardware.

In this guide, we’ll walk through the step-by-step process of using llama.cpp to run LLaMA models locally. We’ll cover what it is, understand how it works, and troubleshoot some of the errors that we may encounter while creating a llama.cpp project.

Let’s start by discussing what llama.cpp is and its key features.

What is llama.cpp?

llama.cpp is a C++ implementation of Meta’s LLaMA models designed for high efficiency and local execution. It allows us to run LLaMA models on a variety of platforms—Windows, macOS, and Linux—without the need for powerful GPUs or external dependencies.

Running LLaMA models locally gives us complete control over data privacy, performance tuning, and costs. We’re not sending sensitive data to third-party servers or paying for API usage. It’s especially beneficial for developers, researchers, and enthusiasts working on personalized AI applications.

Key features:

  • Cross-platform support: Works on Linux, Windows, and macOS.
  • Optimized for CPUs: No GPU required to run models.
  • Quantized models: Reduced memory usage with minimal performance loss.
  • Python bindings: Easily integrates with Python using llama-cpp-python.
  • Community support: Actively maintained with frequent updates.

Next, let’s understand how llama.cpp works.

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How llama.cpp works

Understanding how llama.cpp works under the hood helps us appreciate its efficiency, speed, and why it’s an efficient tool for running LLaMA models locally—especially on consumer-grade hardware without relying on GPUs.

At its heart, llama.cpp is a lightweight, CPU-optimized inference engine built in C++ that enables the use of Meta’s LLaMA language models entirely offline, with low resource requirements. It focuses on:

  • Quantization to reduce model size
  • Memory-mapped inference for efficiency
  • Multithreading to utilize all CPU cores

Let’s learn more about these focus points one by one.

Model quantization (Compression)

Large models like LLaMA 2 are gigabytes in size when using full-precision floats (FP16/FP32). To make them usable on machines with limited RAM or no GPU, llama.cpp supports quantized models in GGUF format.

These quantized models reduce memory usage and computation by using 4-bit, 5-bit, or 8-bit integers, e.g.:

  • Q4_0, Q5_1, Q8_0 — different levels of quantization
  • Smaller size means faster load time and lower RAM footprint

Memory mapping with mmap

llama.cpp uses memory mapping (mmap) to load models efficiently. Instead of loading the whole model into RAM, it streams only the parts needed at any moment. This:

  • Minimizes memory usage
  • Speeds up inference
  • Makes large models possible on modest hardware

Tokenization and inference

Here’s what happens when we send a prompt:

  • Tokenization: The text prompt is broken into tokens using LLaMA’s tokenizer.
  • Feed forward: These tokens are passed through the neural network layers (transformers).
  • Sampling: The model samples the next token using parameters like temperature, top_p, and stop.
  • Decoding: Tokens are converted back to human-readable text.

This loop continues until the desired number of tokens is reached or a stop condition is met.

CPU multithreading

llama.cpp uses multithreading to parallelize computations across multiple CPU cores. We can configure the number of threads with:

llm = Llama(model_path="...", n_threads=8)

This allows faster generation, especially on modern multi-core CPUs.

Now that we know how llama.cpp works, let’s learn how we can install llama.cpp on our local machine in the next section.

How to install llama.cpp locally

Before we install llama.cpp locally, let’s have a look at the prerequisites:

After downloading and installing the prerequisites, we start the llama.cpp installation process.

Step 1: Create a virtual environment

A virtual environment is an isolated workspace within our system where we can install and manage Python packages independently of other projects and the system-wide Python installation. This is particularly helpful while working on multiple Python projects that may require different versions of packages or dependencies.

To create a virtual environment on the local machine, run this command in the terminal:

conda create --name vir-env

Conda is an open-source environment management system primarily used for managing Python and R programming language environments. It comes bundled with the Anaconda distribution.

In the command, we’ve used conda create to create a virtual environment named vir-env, specified by the --name flag.

Step 2: Activate the virtual environment

Activate the newly created virtual environment vir-env using the conda activate command:

conda activate vir-env

Step 3: Install the llama-cpp-python package

The llama-cpp-python package is a Python binding for LLaMA models. Installing this package will help us run LLaMA models locally using llama.cpp.

Let’s install the llama-cpp-python package on our local machine using pip, a package installer that comes bundled with Python:

pip install llama-cpp-python

Next, let’s discuss the step-by-step process of creating a llama.cpp project on the local machine.

How to create a llama.cpp project

Follow these steps to create a llama.cpp project locally:

Step 1: Download a LLaMA model

The first step is to download a LLaMA model, which we’ll use for generating responses. The models compatible with llama.cpp are listed in the TheBloke repository on Hugging Face. For this tutorial, we’ll download the Llama-2-7B-Chat-GGUF model from its official documentation page.

After downloading, open the terminal and run these commands to create some folders on the local machine:

mkdir demo
cd demo
mkdir models
cd models
mkdir llama-2

After creating these folders, save the downloaded model in the llama-2 folder.

Note: Make sure you follow this folder structure, else things may not work as intended.

Step 2: Create a Python script

The next step is to create a Python script that we will use to configure and use the model for generating responses.

So, let’s create a file named llama.py:

touch llama.py

After creating the file, open the file in a code editor and insert this Python script:

from llama_cpp import Llama


# Load the model
llm = Llama(
model_path="./models/llama-2/llama-2-7b-chat.Q4_K_M.gguf",
n_ctx=512,
n_threads=4
)


# Provide a prompt
prompt = "What is Python?"


# Generate the response
output = llm(prompt, max_tokens=250)


# Print the response
print(output["choices"][0]["text"].strip())

The first line in this script imports the Llama class from the llama-cpp-python package. It’s the main interface for loading and interacting with the model. Besides that, we’ve used some parameters:

  • model_path: The path to the model file.
  • n_ctx: The maximum number of tokens the model can handle per prompt. A larger value allows longer context but uses more memory.
  • n_threads: The number of CPU threads to use during inference. We need to set this based on our system’s capabilities (e.g., 4 to 8 for modern CPUs).
  • prompt: The prompt for which we want to generate a response.
  • max_tokens: Restricts the length of the output to the specified number of tokens (250 in this case). Here, the term ‘token’ refers to words or parts of words.

Moreover, the last line extracts the actual response text from the output dictionary and prints it. In this line:

  • output["choices"][0]["text"]: Gets the text result from the first completion.
  • .strip(): Removes any leading/trailing whitespace.

After saving the file, we move on to the next step.

Step 3: Run the script

Finally, it’s time to run the script:

./llama.py

Here is the generated response for our prompt:

Python is a powerful, general-purpose programming language known for its clear syntax and ease of use. Whether you're building a website, analyzing data, or automating tasks, Python provides the tools and libraries to do it efficiently.

In the next section, we’ll check out some common errors that we may face while creating a llama.cpp project.

Common errors while creating a llama.cpp project

Here are some common errors and their solutions:

Missing dependencies

Error: cmake: command not found or Python module errors

Fix: Install required dependencies:

sudo apt install cmake build-essential python3

Incorrect or unsupported compiler

Error: Using an old version of GCC/Clang that doesn’t support modern C++ standards (e.g., C++17).

Fix: Use at least GCC 10+ or Clang 11+.

File not found

Error: file not found: ggml-model.bin

Fix: Check the file path, case sensitivity, and whether the model is actually downloaded and in the correct format (.gguf).

Out-of-memory errors

Error: Segmentation faults or memory allocation failures.

Fix: Reduce context size and use smaller models.

Conclusion

Using llama.cpp to run LLaMA models locally is a game-changer. We gain privacy, speed, and flexibility without depending on expensive hardware or external APIs. Whether we’re building chatbots, summarization tools, or custom NLP workflows, llama cpp gives us the power to bring LLaMA to our local environment.

If you want to learn more about large language models (LLMs), check out the Intro to Large Language Models (LLMs) course on Codecademy.

Frequently asked questions

1. What are the system requirements for running llama.cpp efficiently?

We recommend a minimum RAM of 8 GB for running basic models using llama.cpp. More RAM (16 GB+) allows us to run larger models or multiple instances. A modern CPU with AVX2 support boosts performance, but GPU is optional.

2. What is the difference between llama.cpp and other LLM frameworks?

Unlike heavy frameworks like Hugging Face Transformers, llama.cpp is minimal and optimized for CPU execution. It supports quantized models, reducing memory usage significantly. Tools like llama-cpp-python offer Python compatibility, bridging performance with usability.

3. How does llama.cpp handle updates and improvements in LLaMA models?

The llama.cpp community actively updates the codebase to support newer LLaMA model versions and features. Updates often include performance optimizations, new quantization formats, and bug fixes.

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