computer science

Llama.cpp

Llama.cpp is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Llama.cpp rather than just read about it. In short: llama.cpp is an open-source software library that performs inference on various large language models such as Meta's Llama model. It is co-developed alongside the GGML project, a general-purpose tensor library.

Key takeaways

Llama.cpp belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Llama.cpp to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Llama.cpp from memory before moving on to harder problems.

Reference excerpt

llama.cpp is an open-source software library that performs inference on various large language models such as Meta's Llama model. It is co-developed alongside the GGML project, a general-purpose tensor library. Command-line tools are included with the library, alongside a server with a simple web interface. llama.cpp has been considered as the de facto standard as the core of almost all local inference tools, including Ollama and LM Studio.

Background Towards the end of September 2022, Georgi Gerganov started work on the GGML library, a C library implementing tensor algebra. Gerganov developed the library with the intention of strict memory management and multi-threading. The creation of GGML was inspired by Fabrice Bellard's work on LibNC. Before llama.cpp, Gerganov worked on a similar library called whisper.cpp which implemented Whisper, a speech to text model by OpenAI.

Development llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a project goal. llama.cpp gained traction with users lacking specialized hardware, as it could run on just a CPU. While initially designed for CPUs, GPU and NPU backend support was later added. As of May 2026, it has more than 109,000 stars on GitHub. On April 30, 2024, FlashAttention was introduced. On April 10, 2025, libmtmd was introduced, which reinvigorated support for multimodal models that had previously been stagnant. On December 17, 2025, full acceleration on Android and ChromeOS devices was introduced via a new GUI binding, which unlocks native app development beyond the previous approach of cross-compiling and running CLI in an adb shell.

Architecture llama.cpp supports multiple hardware targets, including x86, ARM, Metal, BLAS, BLIS, zDNN, ZenDNN, SYCL, MUSA, CUDA, HIP, CANN, OpenCL, RPC and Vulkan (version 1.2 or greater). These back-ends make up the GGML tensor library, used by the front-end model-specific llama.cpp code. llama.cpp makes use of several CPU extensions for optimization:

AVX, AVX2, AVX-512, AVX-VNNI and AMX for X86-64. Neon, i8MM, SVE, SVE2, SME and SME2 for AArch64 (ARM64). VXE2 (Vector Enhancement Facility 2) for S390x. Apple silicon is an important target for the project. llama.cpp supports a variety of features aimed at inference on edge devices, such as:

Ahead of time model quantization and on-the-fly kv-cache quantization. Speculative decoding. Partial offloading of model layers to system RAM, allowing devices to load models that would be too large to fit solely in GPU VRAM. In addition, llama.cpp supports a variety of features and APIs for frontend communication, such as:

OpenAI-compatible endpoints like v1/chat/completions. Grammar-based output formatting as JSON.

GGUF file format

The GGUF (GGML Universal File) file format is a binary format that stores both tensors and metadata in a single file, and is designed for fast saving, and loading of model data. It was introduced in August 2023 by the llama.cpp project to better maintain backwards compatibility as support was added for other model architectures. It superseded previous formats used by the project such as GGML and is typically produced by converting models developed with a different machine learning library such as PyTorch.

Design GGUF focuses on quantization, the act of reducing precision in the model weights. This can lead to reduced memory usage and increased speed, albeit at the cost of reduced model accuracy. GGUF supports 2-bit to 8-bit quantized integer types, common floating-point data formats such as float32, float16, and bfloat16, and 1.58-bit quantization. GGUF contains information necessary for running a GPT-like language model such as the tokenizer vocabulary, context length, tensor info and other attributes.

Byte-level structure (little-endian)

Metadata block

Tensors info block

Models Llama.cpp supports many large language models, including Llama, Mistral, Gemma, DeepSeek, gpt-oss, Phi and Qwen.

See also LM Studio — desktop application for locally running and interacting with LLMs Ollama SGLang – framework for structured generation and high-performance large language model inference and serving vLLM – large language model inference and serving engine

References

Illustrations

Worked examples

Example 1 — a first encounter with Llama.cpp

Start with the simplest possible case. Write down what Llama.cpp claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Llama.cpp before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Llama.cpp ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Llama.cpp

In research: Llama.cpp appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Llama.cpp in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Llama.cpp is common in secondary-school and first-year university syllabi. It links to neighbouring topics Free computer libraries, Free software programmed in C++, Large language models, so understanding it makes those chapters shorter.
In everyday life: Look for Llama.cpp outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Llama.cpp in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Llama.cpp means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Llama.cpp out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Llama.cpp in simple terms?

Why does Llama.cpp matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Llama.cpp?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Llama.cpp.