Llama.cpp Setup on Raspberry Pi 5

Introduction: Local LLMs, No Cloud Required

I set up Llama.cpp on a Raspberry Pi 5 last week, and guess what? It actually worked. No cloud, no subscription, no spying — just me, a Pi, and a model file on my desk. If you’ve ever wanted a chatbot that doesn’t need a server farm or a monthly invoice, this is your shot.

Now, before you get any wild ideas about running GPT-4 on a $60 board, let’s pump the brakes. We’re talking about quantized LLMs, trimmed-down versions that trade size for speed. These run locally, use a GGUF model format, and need around 4 to 8 GB of RAM, depending on the model. The Pi 5’s ARM Cortex-A76 cores and USB 3.0 ports actually make it capable of handling small models like the 7B variant of LLaMA.

And yes, you’ll be living in the terminal for a bit. This setup uses Git, CMake, g++, and just enough Linux commands to feel like you’re doing something cool — but not enough to break anything major. I’ll walk you through how to build Llama.cpp from source, get your swap file adjusted, and run your first local prompt without frying your Pi.

Let’s get started.

Key Takeaways

• Llama.cpp runs surprisingly well on a Raspberry Pi 5 if you use the right model and cooling.
• You need a quantized GGUF file, swap configured, and basic Linux tools installed.
• Local LLMs respect your privacy and don’t rely on cloud APIs or subscriptions.
• With proper tuning, a Pi 5 can act as your private, offline AI assistant for chat, coding, and note help.

System Requirements and Hardware Overview

Raspberry Pi 5 Minimum Specs
Llama.cpp needs some muscle, even in its lightweight form. You’ll want the Raspberry Pi 5 with at least 4GB of LPDDR4X RAM, though 8GB makes life a lot easier. The processor is a quad-core ARM Cortex-A76, and for once, it doesn’t feel like a bottleneck.

Recommended RAM and Cooling Setup
Running a local LLM eats memory. Even a quantized 7B model can nudge the 4GB line. That’s where swap space helps — we’ll set that up soon. But heat? That’s your real enemy. If you’re not using a fan or heatsink, the CPU will hit its thermal limit faster than your patience on hold with tech support. Invest in active cooling or a passive aluminum case.

Storage Space: microSD vs. SSD
Technically, you can run everything from a microSD card, but it’ll be slower. Really slow. And model files can be several gigabytes. If you’ve got a spare USB 3.0 SSD, use that. It’ll make download, load, and token processing times a lot better.

Component	Minimum	Recommended
RAM	4GB LPDDR4X	8GB LPDDR4X
Cooling	Heatsink	Active fan or metal case
Storage	32GB microSD (U3 or A2)	64GB+ USB 3.0 SSD
Power Supply	5V/5A USB-C	Official Pi 5 adapter

Power Supply and Heat Management
Power draw climbs when Llama.cpp is generating tokens. Stick with a 5V/5A USB-C adapter (the official one is safest). Cheap adapters often dip voltage, which leads to throttling. You might not see it at first, but it adds up when your Pi quietly cooks itself mid-prompt.

Preparing Raspberry Pi OS for Compilation

Update and Upgrade Everything
Start with a fresh install of Raspberry Pi OS 64-bit. If you’re already using the Pi for other stuff, fine — but at least make sure it’s updated:

sudo apt update && sudo apt upgrade -y

After that, reboot. Trust me, it avoids half the errors you’ll find on forums later.

Install Required Dependencies
Llama.cpp is written in C/C++ and needs a few tools to compile. You’ll need:

• git (for cloning the repo)
• cmake (to generate build files)
• g++ (to compile the code)
• build-essential (includes common development packages)

Run this all at once:

sudo apt install git cmake g++ build-essential -y

If you skip this, your cmake or make command will just sit there, blinking at you like you broke something.

Expand Your Swap File
By default, the Pi only allocates 100MB of swap space. That’s not enough. If you’re using a 4GB Pi, this step is mandatory or your build process will choke.

Edit the swap config:

sudo nano /etc/dphys-swapfile

Find the line:

CONF_SWAPSIZE=100

Change it to something like:

CONF_SWAPSIZE=2048

Then apply the change:

sudo systemctl restart dphys-swapfile

If you’re compiling larger models or using the Pi for other tasks, go higher — 4096MB is a safe max on good SD cards or SSDs.

Enable SSH for Remote Builds (Optional)
If you hate working directly on the Pi’s small screen or don’t even have a monitor, enable SSH:

sudo raspi-config

Navigate to Interface Options > SSH > Enable. You can then log in from another machine using:

ssh pi@your-local-ip

Use this if you want to do the build from a laptop but keep the Pi in the corner running.

Cloning and Compiling Llama.cpp

Clone the Repository from GitHub
Head to your terminal and grab the latest version of Llama.cpp straight from the source. This is where the command-line magic starts:

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

This will create a folder called llama.cpp in your current directory with all the source files.

Generate Build Files with CMake
Now it’s time to prep for compilation. Inside the llama.cpp folder, run:

mkdir build
cd build
cmake ..

CMake creates the config files that tell the compiler what to do. If you don’t see any errors, you’re in good shape.

Compile with Make
Once CMake finishes, fire up make:

make

This will take a few minutes, especially on a Pi 5. You’ll see the terminal fill with lines like:

[ 12%] Building CXX object ...

If you get any errors, go back and check your dependencies or swap setup. A failed build is usually traced to either RAM or a missing compiler.

Confirm It’s Working
Once it’s built, you should see an executable called main inside the build folder. Test it:

./main --help

You should get a usage message. That means the Llama.cpp CLI tool compiled correctly and you’re ready to load a model.

Choosing and Downloading a GGUF Model

What is GGUF and Why It Matters
GGUF stands for GPT Grammar Universal Format. It’s a binary model format specifically designed for Llama.cpp and similar projects. It’s compact, optimized, and ready to run on hardware with limited RAM like the Raspberry Pi 5. You can’t just use any .bin file — it needs to be quantized and in GGUF format.

Model Sizes: What Your Pi Can Handle

• 7B models (7 billion parameters) are the most common and are reasonable to run with 4GB+ of RAM, especially when quantized.
• 13B models can technically load on an 8GB Pi, but performance will be sluggish.
• Smaller models like TinyLLaMA or Phi may perform better if you need speed over depth.

The most popular quantization types for the Pi are:

• Q4_0 – lower memory, faster, less accurate
• Q5_1 – a balance between quality and speed

Where to Find GGUF Files
The best source is Hugging Face, especially from trusted uploaders like TheBloke. These are already converted, quantized, and tested.

Direct example for a 7B model:
TheBloke/Llama-2-7B-GGUF

How to Download the Model
You can use a browser on another machine, then move the file over to your Pi via USB or SCP.

Or use wget on the Pi directly. Find the *.gguf file URL and run:

wget https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf

Organize the Files
Once downloaded, make a models folder:

mkdir ~/llama-models
mv llama-2-7b.Q4_0.gguf ~/llama-models/

Make sure the path is clean and easy to reference later when you start prompting.

Running a Test Prompt in the Terminal

Create a Simple Prompt File
Llama.cpp expects a prompt to feed the model. You can pipe it in directly, but it’s cleaner to make a text file. For example:

nano prompt.txt

Add something simple like:

What is the capital of France?

Save it with CTRL + O, then exit with CTRL + X.

Run the Model Using the CLI
Assuming your .gguf model is in ~/llama-models, here’s the basic command:

./main -m ~/llama-models/llama-2-7b.Q4_0.gguf -f prompt.txt -n 100 -t 4

Breakdown:

• -m = path to model
• -f = prompt file
• -n = number of output tokens (try 100 to start)
• -t = number of threads (use 4 on a Pi 5)

If your model loads, you’ll see the token generation process begin, something like:

llama_model_load: loading model from 'llama-2-7b.Q4_0.gguf' - please wait ...
...
llama_print_timings: prompt eval time = 1234 ms

Tweak Output Settings
Here are some optional flags you can experiment with:

• --temp 0.7 – changes randomness (lower is more predictable)
• --top-k 40 – narrows sampling range
• --repeat-penalty 1.1 – prevents repetition

Full command with tweaks:

./main -m ~/llama-models/llama-2-7b.Q4_0.gguf -f prompt.txt -n 150 -t 4 --temp 0.7 --repeat-penalty 1.1 --top-k 40

Interpret Output & Speed
Don’t expect blazing speed — on the Pi 5, you’ll get around 5 to 10 tokens per second with quantized 7B models. That’s usable for questions, small replies, and debugging. It’s not instant, but it’s fully local and private.

Managing RAM and Improving Speed

Understanding Token Load vs. RAM
Every token your model processes adds to RAM usage. On a 7B GGUF model, even quantized, running a long prompt with high token output can spike usage above 3GB. With only 4GB onboard, you’ll be depending heavily on that swap file.

Keep your -n (tokens to generate) modest — somewhere between 50 to 150 tokens — until you find your Pi’s sweet spot.

Quantization Level Affects Performance
Lower quantization like Q4_0 uses less memory but sacrifices accuracy. Going up to Q5_1 gives better answers but increases RAM usage. Don’t bother with full-precision models unless you just like watching your Pi throttle.

Add or Adjust Swap for Stability
If your Pi locks up, it’s likely out of RAM. Here’s how to check swap use live:

htop

Look at the SWAP bar. If it’s pinned at 100%, either add more swap or reduce your output tokens.

To increase it to 4GB:

sudo nano /etc/dphys-swapfile
# Set CONF_SWAPSIZE=4096
sudo systemctl restart dphys-swapfile

Use Multi-threading Wisely
The Pi 5 has 4 performance cores. You can use up to -t 4 safely. Using fewer threads saves heat and power, but slows down output. More threads = more tokens/sec, until you hit thermal or memory limits.

Here’s a speed comparison:

Threads	Avg. Tokens/sec (Q4_0, 7B)
1	2.5
2	4.0
4	7.5–9.0

Monitor Temps and CPU Load
Your Pi will get warm under load. To monitor temps, run:

vcgencmd measure_temp

If it regularly hits 80°C, you’re throttling. Add cooling or lower thread count. Avoid using a closed plastic case — it traps heat like a toaster.

Troubleshooting Common Errors

CMake or G++ Not Found
If you run cmake .. or make and get a “command not found” error, it usually means a missing dependency. Go back and install them:

sudo apt install cmake g++ build-essential -y

Swap Errors or System Freezes
When you get a blank screen, stuttering, or total system lockup during model load, you’re likely out of RAM and have no swap configured. Double-check your swap size:

free -h

If swap is still 100MB, you forgot to restart the service. Run:

sudo systemctl restart dphys-swapfile

Model File Not Found
If you see:

error loading model: failed to open

You either mistyped the path or moved the file after the build. Confirm the path:

ls ~/llama-models

And rerun with the correct filename.

Permission Denied
Trying to run ./main but get a permission error? Make it executable:

chmod +x main

Segmentation Fault or Crash on Load
This is usually a memory issue, especially with large models or no swap. Try:

• Using a smaller model (7B)
• Lowering the context size (--ctx-size 512)
• Increasing swap size to 4096MB

Also, avoid loading directly from a microSD card under stress. A USB 3.0 SSD is much more stable.

Optional Upgrades and Integrations

Using VSCode for Editing and SSH Access
If you’re more comfortable with a visual editor, use Visual Studio Code with the Remote – SSH extension. Once SSH is enabled on your Pi, connect using:

ssh pi@your-local-ip

Now you can edit prompt files, scripts, or even browse system folders from your main computer without touching the Pi physically.

Running Llama.cpp with Python Bindings
If you’re building more than a chatbot and want to script interactions, you can use Python bindings for Llama.cpp. There are community-supported wrappers like:

• llama-cpp-python: install via pip and link your model

pip install llama-cpp-python

This opens the door to writing simple Python apps, chatbots, or integrations with local tools like home automation or note-taking apps.

Adding a Web Interface (Optional Frontend)
Some folks don’t like the command line. There are browser-based UIs like:

• text-generation-webui
• llamafile’s mini-server mode
• llm-webui

Most require more RAM and resources, but with the Pi 5’s PCIe support, you can run them if you’ve got an SSD and good cooling.

Access Over Local Network via API
For those with coding chops, use the server mode of Llama.cpp (server binary) to spin up a REST-like local endpoint. Then access it from other devices on your LAN. It’s not secured, so avoid doing this over the internet.

Example:

./server -m ~/llama-models/llama-2-7b.Q4_0.gguf -c 512 -ngl 1

Then you can send prompts via curl or a simple frontend.

Keeping Things Private and Offline

Why Run AI Locally?
When you install and use Llama.cpp on a Raspberry Pi 5, your data never leaves the room. You’re not sending prompts to an API. You’re not feeding someone else’s machine learning pipeline. It’s your words, your device, your control.

Zero Cloud, Zero Logging
Unlike most hosted LLMs, local models don’t record your inputs or store conversations. There’s no need to sign in, no telemetry, and no tracking. You type a prompt, the model generates a reply — that’s it.

Control Network Access
If you’re serious about staying offline:

• Disconnect Wi-Fi once the model is downloaded
• Use a local-only SSH session
• Block internet traffic with ufw (Uncomplicated Firewall)

Example setup:

sudo apt install ufw
sudo ufw default deny outgoing
sudo ufw enable

No Subscription, No Account, No Limits
Open-source tools like Llama.cpp don’t require login credentials or billing details. There’s no usage cap, no character limit, and no server outages. You’re free to experiment, test, and tinker without worrying about tokens or rate limits.

Perfect for Secure Environments
If you’re running this in a classroom, lab, or private network, this kind of offline AI is ideal. It respects your boundaries and doesn’t ping external servers every time it thinks of a response.

What Can You Actually Do with This?

Local AI Chatbot
With Llama.cpp running on your Pi 5, you’ve got a functional chatbot that doesn’t need Wi-Fi or a cloud API. Ask questions, simulate conversations, or get help drafting notes — all with minimal latency and no data leakage.

Coding Helper
Feed it programming questions or snippets, and it can provide syntax help, suggestions, or even walk through logic. No connection to Stack Overflow required. It’s not perfect, but it’s decent at boilerplate and general logic assistance.

Summarizing Long Texts
Drop a chunk of text into your prompt, and the model can generate a summary. It works best on smaller input sizes, but with some trimming or batching, it can handle articles, logs, or even meeting notes.

Personal Note Assistant
Use it to rephrase, expand, or brainstorm text. Ask it to revise sentences, create outlines, or format ideas. You can even combine it with speech-to-text tools for a lightweight productivity setup.

Home Lab AI
If you’re into smart home stuff, it’s possible to integrate your Pi-hosted model into a local automation setup. Use a Python script to handle inputs from home sensors or routines, and feed them into your model for decisions or alerts.

FAQs

Can I run this on a Raspberry Pi 4?
Technically yes, but performance will suffer. The Pi 5’s faster CPU and better thermal handling make a big difference.

Do I need internet to use Llama.cpp after setup?
Nope. Once you’ve installed the model, you can disconnect and run it fully offline.

What’s the smallest model that actually works?
TinyLLaMA or a 3B GGUF model works faster than a 7B, but the 7B Q4_0 strikes a solid balance between speed and response quality.

How much space do I need?
Plan for at least 8GB free just for the model. Add more for OS, build files, and future models — a 64GB drive is a safe bet.

Can I build a web-based interface on top of this?
Yes. You can run server mode and interact with it over your LAN via scripts or a minimal web frontend.

References

• Llama.cpp GitHub
• GGUF Model Format
• Hugging Face – TheBloke Models
• Raspberry Pi 5 Specs
• Raspberry Pi OS Download