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In the rapidly evolving landscape of artificial intelligence, understanding the distinctions between various tools and models is crucial for developers and researchers. This blog post aims to elucidate the differences between the LLaMA model, llama.cpp, and Ollama. While the LLaMA model serves as the foundational large language model developed by Meta, llama.cpp is an open-source C++ implementation designed to run LLaMA efficiently on local hardware. Building upon llama.cpp, Ollama offers a user-friendly interface with additional optimizations and features. By exploring these distinctions, readers will gain insights into selecting the appropriate tool for their AI applications.

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What is the LLaMA Model?

LLaMA (Large Language Model Meta AI) is a series of open-weight large language models (LLMs) developed by Meta (formerly Facebook AI). Unlike proprietary models like GPT-4, LLaMA models are released under a research-friendly license, allowing developers and researchers to experiment with state-of-the-art AI while maintaining control over data and privacy.

LLaMA models are designed to be smaller and more efficient than competing models while maintaining strong performance in natural language understanding, text generation, and reasoning.

LLaMA is a Transformer-based AI model that processes and generates human-like text. It is similar to OpenAI’s GPT models but optimized for efficiency. Meta’s goal with LLaMA is to provide smaller yet powerful language models that can run on consumer hardware.

Unlike GPT-4, which is closed-source, LLaMA models are available to researchers and developers, enabling:

• Customisation & fine-tuning for specific applications
• Running models locally instead of relying on cloud APIs
• Improved privacy since queries don’t need to be sent to external servers

LLaMA models are powerful, but they are not the only open-source LLMs available. Let’s compare them with other major models:

Feature	LLaMA 2	GPT-4 (OpenAI)	Mistral 7B	Mixtral (MoE)
Size	7B, 13B, 70B	Proprietary	7B	12.9B (MoE)
Open-Source?	✅ Yes	❌ No	✅ Yes	✅ Yes
Performance	GPT-3.5 Level	🔥 Best	Better than LLaMA 2-7B	Outperforms LLaMA 2-13B
Fine-Tunable?	✅ Yes	❌ No	✅ Yes	✅ Yes
Runs on CPU?	✅ Yes (with llama.cpp)	❌ No	✅ Yes	❌ Requires GPU
Best For	Chatbots, research, AI apps	General AI, commercial APIs	Fast reasoning, efficiency	Scalable AI applications

LLaMA models are versatile and can be used for various applications:

• AI Chatbots
• Code Generation
• Scientific Research
• Private AI Applications

LLaMA is one of the most influential open-weight LLMs, offering a balance between power, efficiency, and accessibility. Unlike closed-source models like GPT-4, LLaMA allows developers to run AI locally, fine-tune models, and ensure data privacy.

AI Model Quantisation: Making AI Models Smaller and Faster

AI models, especially deep learning models like large language models (LLMs) and speech recognition systems, are huge. They require massive amounts of computational power and memory to run efficiently. This is where model quantisation comes in—a technique that reduces the size of AI models and speeds up inference while keeping accuracy as high as possible.

Quantisation is the process of converting a model’s parameters (weights and activations) from high-precision floating-point numbers (e.g., 32-bit float, FP32) into lower-precision numbers (e.g., 8-bit integer, INT8). This reduces the memory footprint and improves computational efficiency, allowing AI models to run on less powerful hardware like CPUs, edge devices, and mobile phones.

When an AI model is trained, it typically uses 32-bit floating-point (FP32) numbers to represent its weights and activations. These provide high precision but require a lot of memory and processing power. Quantisation converts these high-precision numbers into lower-bit representations, such as:

• FP32 → FP16 (Half-precision floating-point)
• FP32 → INT8 (8-bit integer)
• FP32 → INT4 / INT2 (Ultra-low precision)

The lower the bit-width, the smaller and faster the model becomes, but at the cost of some accuracy. Assume we have a weight value stored as a 32-bit float:

Weight (FP32) = 0.87654321

If we convert this to 8-bit integer (INT8):

Weight (INT8) ≈ 87 (scaled down)

Even though we lose some precision, the model remains usable while consuming much less memory and processing power.

There are several types of quantisation:

• Post-Training Quantisation – PTQ (Applied after training, converts model weights and activations to lower precision, faster but may cause some accuracy loss)
• Quantisation-Aware Training – QAT (The model is trained while simulating lower precision, maintains higher accuracy compared to PTQ, more computationally expensive during training, used when accuracy is critical e.g., in medical AI models)
• Dynamic Quantisation (Only weights are quantised; activations remain in higher precision, applied at runtime, making it more flexible, used in NLP models like llama.cpp for efficient inference)
• Weight-Only Quantisation (Only model weights are quantised, not activations, used in GGUF/GGML models to run LLMs efficiently on CPUs)

Some of the benefits of quantisation are:

• Reduces Model Size – Helps fit large AI models on small devices.
• Speeds Up Inference – Allows faster processing on CPUs and edge devices.
• Lower Power Consumption – Essential for mobile and embedded applications.
• Enables AI on Consumer Hardware – Allows running LLMs (like llama.cpp) on laptops and smartphones.

Real world examples of quantisation include:

• Whisper.cpp – Uses INT8 quantisation for speech-to-text transcription on CPUs.
• Llama.cpp – Uses GGUF/GGML quantisation to run LLaMA models efficiently on local machines.
• TensorFlow Lite & ONNX – Deploy AI models on mobile and IoT devices using quantized versions.

Quantisation is one of the most effective techniques for optimising AI models, making them smaller, faster, and more efficient. It allows complex deep learning models to run on consumer-grade hardware without sacrificing too much accuracy. Whether you’re working with text generation, speech recognition, or computer vision, quantisation is a game-changer in bringing AI to the real world.

Model fine-tuning with LoRA

Low-Rank Adaptation (LoRA) is a technique introduced to efficiently fine-tune large-scale pre-trained models, such as Large Language Models (LLMs), for specific tasks without updating all of their parameters. As models grow in size, full fine-tuning becomes computationally expensive and resource-intensive. LoRA addresses this challenge by freezing the original model’s weights and injecting trainable low-rank matrices into each layer of the Transformer architecture. This approach significantly reduces the number of trainable parameters and the required GPU memory, making the fine-tuning process more efficient.  

In traditional fine-tuning, all parameters of a pre-trained model are updated, which is not feasible for models with billions of parameters. LoRA proposes that the changes in weights during adaptation can be approximated by low-rank matrices. By decomposing these weight updates into the product of two smaller matrices, LoRA introduces additional trainable parameters that are much fewer in number. These low-rank matrices are integrated into the model’s layers, allowing for task-specific adaptation while keeping the original weights intact.  

LoRA presents several advantages:

• Parameter Efficiency: LoRA reduces the number of trainable parameters by orders of magnitude. For instance, fine-tuning GPT-3 with LoRA can decrease the trainable parameters by approximately 10,000 times compared to full fine-tuning.  
• Reduced Memory Footprint: By updating only the low-rank matrices, LoRA lowers the GPU memory requirements during training, making it feasible to fine-tune large models on hardware with limited resources.  
• Maintained Performance: Despite the reduction in trainable parameters, models fine-tuned with LoRA perform on par with, or even better than, those fine-tuned traditionally across various tasks.  

LoRA has been applied successfully in various domains, including:

• Natural Language Processing (NLP): Fine-tuning models for specific tasks like sentiment analysis, translation, or question-answering.
• Computer Vision: Adapting vision transformers to specialised image recognition tasks.
• Generative Models: Customising models like Stable Diffusion for domain-specific image generation.

By enabling efficient and effective fine-tuning, LoRA facilitates the deployment of large models in specialised applications without the associated computational burdens of full model adaptation.

Using llama.cpp to Run Large Language Models Locally

With the rise of large language models (LLMs) like OpenAI’s GPT-4 and Meta’s LLaMA series, the demand for running these models efficiently on local machines has grown. However, most large-scale AI models require powerful GPUs and cloud-based services, which can be costly and raise privacy concerns.

Enter llama.cpp, a highly optimised C++ implementation of Meta’s LLaMA models that allows users to run language models directly on CPUs. This makes it possible to deploy chatbots, assistants, and other AI applications on personal computers, edge devices, and even mobile phones—without relying on cloud services.

What is llama.cpp?

llama.cpp is an efficient CPU-based inference engine for running Meta’s LLaMA models (LLaMA 1, LLaMA 2, and variants like Mistral, Phi, and Qwen) on Windows, macOS, Linux, and even ARM-based devices. It uses quantisation techniques to reduce the model size and memory requirements, making it possible to run LLMs on consumer-grade hardware.

The key features of llama.cpp are:

• CPU-based execution – No need for GPUs.
• Quantisation support – Reduces model size with minimal accuracy loss.
• Multi-platform – Runs on Windows, Linux, macOS, Raspberry Pi, and Android.
• Memory efficiency – Optimised for low RAM usage.
• GGUF format – Uses an efficient binary format for LLaMA models.

Installing llama.cpp

The minimum system requirements for llama.cpp are:

• OS: Windows, macOS, or Linux.
• CPU: Intel, AMD, Apple Silicon (M1/M2), or ARM-based processors.
• RAM: 4GB minimum, 8GB+ recommended for better performance.
• Dependencies: gcc, make, cmake, python3, pip

To install on Linux/macOS, first clone the repository:

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

Then, build the project:

make

This compiles the main executable for CPU inference.

On Windows, install MinGW-w64 or use WSL (Windows Subsystem for Linux). Then, open a terminal (PowerShell or WSL) and run:

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

Alternatively, you can use Python Bindings. llama.cpp provides Python bindings for easy usage:

pip install llama-cpp-python

Downloading and Preparing Models

Meta’s LLaMA models require approval for access. However, open-weight alternatives like Mistral, Phi, and Qwen can be used freely. To download a model, visit Hugging Face and search for LLaMA 2 GGUF models. Download a quantised model, e.g., llama-2-7b.Q4_K_M.gguf.

If you have raw LLaMA models, you must convert them to the GGUF format. First, install transformers:

pip install transformers

Then, convert:

python convert.py --model /path/to/llama/model

Once you have a GGUF model, you can start chatting!

./main -m models/llama-2-7b.Q4_K_M.gguf -p "Tell me a joke"

This runs inference using the model and generates a response. To run a chatbot session:

./main -m models/llama-2-7b.Q4_K_M.gguf --interactive

It will allow continuous interaction, just like ChatGPT.

If needed, you can quantise a model using one of the available levels:

• Q8_0 – High accuracy, large size.
• Q6_K – Balanced performance and accuracy.
• Q4_K_M – Optimised for speed and memory.
• Q2_K – Ultra-low memory, reduced accuracy.

You can quantise a model using:

python quantize.py --model llama-2-7b.gguf --type Q4_K_M

This produces a GGUF file that is much smaller and runs faster.

To improve performance, use more CPU threads:

./main -m models/llama-2-7b.Q4_K_M.gguf -t 8

This will use 8 threads for inference.

If you have a GPU, you can enable acceleration:

make LLAMA_CUBLAS=1

This allows CUDA-based inference on NVIDIA GPUs.

Fine-tuning

With the power of llama.cpp and LoRA, you can build advanced chatbots, specialised assistants and domain-specific NLP solutions, all running locally, with full control over data and privacy.

Fine-tuning with llama.cpp requires a dataset in JSONL format (JSON Lines), which is a widely-used structure for text data in machine learning. Each line in the JSONL file represents an input-output pair. This format allows the model to learn a mapping from inputs (prompts) to outputs (desired completions):

{"input": "What is the capital of France?", "output": "Paris"}
{"input": "Translate to French: apple", "output": "pomme"}
{"input": "Explain quantum mechanics.", "output": "Quantum mechanics is a fundamental theory in physics..."}

To create a dataset, collect data relevant to your task. For example:

• Question-Answer Pairs – For a Q&A bot.
• Translation Examples – For a language translation model.
• Dialogue Snippets – For chatbot fine-tuning.

Once you have the JSONL dataset ready, you can fine-tune your llama.cpp model using finetune.py. This script utilizes LoRA (Low-Rank Adaptation) to efficiently train the model.

First, you need to install the required libraries:

pip install torch transformers datasets peft bitsandbytes

You can now run finetune.py using the following command:

python finetune.py --model models/llama-2-7b.Q4_K_M.gguf --data dataset.jsonl --output-dir lora-output

After fine-tuning, the LoRA adapters must be merged with the base model to produce a single, fine-tuned model file.

python merge_lora.py --base models/llama-2-7b.Q4_K_M.gguf --lora lora-output --output models/llama-2-7b-finetuned.gguf

You can test the fine-tuned model using llama.cpp to see how it performs:

./main -m models/llama-2-7b-finetuned.gguf -p "What is the capital of France?"

Interesting Models to Run on llama.cpp

There are several models that you can run on llama.cpp:

1. LLaMA 2

• Creator: Meta
• Variants: 7B, 13B, 70B
• Use Cases: General-purpose chatbot, knowledge retrieval, creative writing
• Best Quantized Version: Q4_K_M (balanced accuracy and speed)
• Why It’s Interesting: LLaMA 2 is one of the most powerful open-weight language models, comparable to GPT-3.5 in many tasks. It serves as the baseline for experimentation.

Example Usage in llama.cpp:

./main -m models/llama-2-13b.Q4_K_M.gguf -p "Explain the theory of relativity in simple terms."

2. Mistral 7B

• Creator: Mistral AI
• Variants: 7B (densely trained)
• Use Cases: Chatbot, reasoning, math, structured answers
• Best Quantized Version: Q6_K
• Why It’s Interesting: Mistral 7B is optimized for factual accuracy and reasoning. It outperforms LLaMA 2 in some tasks despite being smaller.

Example Usage:

./main -m models/mistral-7b.Q6_K.gguf -p "Summarize the latest advancements in quantum computing."

3. Mixtral (Mixture of Experts)

• Creator: Mistral AI
• Variants: 12.9B (only 2 experts active at a time)
• Use Cases: High-performance chatbot, research assistant
• Best Quantized Version: Q5_K_M
• Why It’s Interesting: Unlike standard models, Mixtral is a Mixture of Experts (MoE) model, meaning it activates only two out of eight experts per token. This makes it more efficient than similarly sized dense models.

Example Usage:

./main -m models/mixtral-8x7b.Q5_K_M.gguf --interactive

4. Code LLaMA

• Creator: Meta
• Variants: 7B, 13B, 34B
• Use Cases: Code generation, debugging, explaining code
• Best Quantized Version: Q4_K
• Why It’s Interesting: This model is fine-tuned for programming tasks. It can generate Python, JavaScript, C++, Rust, and more.

Example Usage:

./main -m models/code-llama-13b.Q4_K.gguf -p "Write a Python function to reverse a linked list."

5. Phi-2

• Creator: Microsoft
• Variants: 2.7B
• Use Cases: Math, logic, reasoning, lightweight chatbot
• Best Quantized Version: Q5_K_M
• Why It’s Interesting: Despite being only 2.7B parameters, Phi-2 is surprisingly strong in logical reasoning and problem-solving, outperforming models twice its size.

Example Usage:

./main -m models/phi-2.Q5_K_M.gguf -p "Solve the equation: 5x + 7 = 2x + 20."

6. Qwen-7B

• Creator: Alibaba
• Variants: 7B, 14B
• Use Cases: Conversational AI, structured text generation
• Best Quantized Version: Q4_K_M
• Why It’s Interesting: Qwen models are multilingual and trained with high-quality data, making them excellent for chatbots.

Example Usage:

./main -m models/qwen-7b.Q4_K_M.gguf --interactive

Ollama: A Local AI Tool for Running Large Language Models

Ollama is another open-source tool that enables users to run large language models (LLMs) locally on their machines. Unlike cloud-based AI services like OpenAI’s GPT models, Ollama provides a privacy-focused, efficient, and customisable approach to working with AI models. It allows users to download, manage, and execute AI-powered applications on macOS, Linux, and Windows (preview), reducing reliance on external servers.

Ollama supports multiple models, including LLaMA 3.3, Mistral, Phi-4, DeepSeek-R1, and Gemma 2, catering to a range of applications such as text generation, code assistance, and scientific research.

Ollama is easy to install with just a single command (macOS & Linux):

curl -fsSL https://ollama.com/install.sh | sh

Windows support is currently in preview. You can install it by downloading the latest version from the Ollama website.

Once installed, you can run an AI model with one simple command:

ollama run mistral

This command downloads the model automatically (if not already installed) and starts generating text based on the input. You can provide a custom prompt to the model:

ollama run mistral "What are black holes?"

Available AI Models in Ollama

Ollama supports multiple open-weight models. Here are some of the key ones:

1. LLaMA 3.3

General-purpose NLP tasks such as text generation, summarisation, and translation.

Example Command:

ollama run llama3 "Explain the theory of relativity in simple terms."

2. Mistral

Code generation, large-scale data analysis, and fast text-based tasks.

Example Command:

ollama run mistral "Write a Python script that calculates Fibonacci numbers."

3. Phi-4

Scientific research, literature review, and data summarisation.

Example Command:

ollama run phi "Summarise the key findings of quantum mechanics."

4. DeepSeek-R1

AI-assisted research, programming help, and chatbot applications.

Example Command:

ollama run deepseek "What are the ethical considerations of AI in medicine?"

5. Gemma 2

A multi-purpose AI model optimised for efficiency.

Example Command:

ollama run gemma "Generate a short sci-fi story about Mars."

Using Ollama in a Python Script

Developers can integrate Ollama into their Python applications using its OpenAI-compatible API.

import requests

response = requests.post(
    "http://localhost:11434/api/generate",
    json={"model": "mistral", "prompt": "Explain black holes."}
)

print(response.json()["response"])

This allows developers to build AI-powered applications without relying on cloud services.

Advanced Usage

To see which models you have installed:

ollama list

If you want to download a model without running it:

ollama pull llama3

You can start Ollama in server mode for use in applications:

ollama serve

Ollama is a powerful tool for anyone looking to run AI models locally—whether for text generation, coding, research, or creative writing. Its simplicity, efficiency, and privacy-first approach make it an excellent alternative to cloud-based AI services.

Key Differences Between Ollama and llama.cpp

Both Ollama and llama.cpp are powerful tools for running large language models (LLMs) locally, but they serve different purposes. While llama.cpp is a low-level inference engine focused on efficiency and CPU-based execution, Ollama is a high-level tool designed to simplify running LLMs with an easy-to-use API and built-in model management.

If you’re wondering which one to use, next we break down the major differences between Ollama vs. llama.cpp, covering their features, performance, ease of use, and best use cases.

Feature	llama.cpp	Ollama
Primary Purpose	Low-level LLM inference engine	High-level LLM runtime with API
Ease of Use	Requires manual setup & CLI knowledge	Simple CLI with built-in model handling
Model Management	Manual	Automatic download & caching
Supported Models	LLaMA, Mistral, Mixtral, Qwen, etc.	Same as llama.cpp, plus model catalog
Quantization Support	Yes (GGUF)	Yes (automatically handled)
Runs on CPU	✅ Yes	✅ Yes
Runs on GPU	❌ (Only with extra setup)	✅ Yes (CUDA-enabled by default)
API Support	❌ No built-in API	✅ Has an OpenAI-compatible API
Web Server Support	❌ No	✅ Yes (serves models via HTTP API)
Installation Simplicity	Requires compiling manually	One-command install
Performance Optimization	Fine-tuned for CPU efficiency	Optimised but with slight overhead due to API layer

llama.cpp is slightly faster on CPU since it is a barebones inference engine with no extra API layers. Ollama has a small overhead because it manages API interactions and model caching.

llama.cpp does not natively support GPU but can be compiled with CUDA or Metal manually. Ollama supports GPU out of the box on NVIDIA (CUDA) and Apple Silicon (Metal).

So, when should you use one or the other?

If you need…	Use llama.cpp	Use Ollama
Maximum CPU efficiency	✅ Yes	❌ No
Easy setup & installation	❌ No	✅ Yes
Built-in API for applications	❌ No	✅ Yes
Manual model control (fine-tuning, conversion)	✅ Yes	❌ No
GPU acceleration out of the box	❌ No (requires manual setup)	✅ Yes
Streaming responses (for chatbot UIs)	❌ No	✅ Yes
Web-based AI serving (like OpenAI API)	❌ No	✅ Yes

If you’re a developer or researcher who wants fine-grained control over model execution, llama.cpp is the better choice. If you just want an easy way to run LLMs (especially with an API and GPU support), Ollama is the way to go.