# ggml

ggml is a simple, portable, efficient tensor library for machine learning, implemented in C/C++ with cross-platform and hardware-backend support.

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## Local LLM Inference : llama.cpp, GGUF, Quantizations and GGML Explained

DevFeed: [Local LLM Inference : llama.cpp, GGUF, Quantizations and GGML Explained](<https://devfeed.tech/articles/local-llm-inference-llama-cpp-gguf-quantizations-and-ggml-explained-35012.md>)

Original publisher: [Read original article](<https://read.theaimerge.com/p/an-ai-engineers-guide-to-running>)

Author: Alex Razvant

Published: 2026-03-03T11:31:04Z

Content type: tutorial

Language: en

Sources: [Neural Bits](<https://devfeed.tech/sources/neural-bits.md>)

Topics: [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [ggml](<https://devfeed.tech/topics/ggml.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [quantization](<https://devfeed.tech/topics/quantization.md>)

Tags: [backend](<https://devfeed.tech/tags/backend.md>), [cross-platform](<https://devfeed.tech/tags/cross-platform.md>), [efficiently](<https://devfeed.tech/tags/efficiently.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [format](<https://devfeed.tech/tags/format.md>), [ggml](<https://devfeed.tech/tags/ggml.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llm](<https://devfeed.tech/tags/llm.md>), [local-llm](<https://devfeed.tech/tags/local-llm.md>), [model](<https://devfeed.tech/tags/model.md>)

### AI overview

A practical guide to local LLM inference with llama.cpp, explaining how the GGUF model format, GGML backend concepts, quantization, and inference workflows fit together for efficient execution on edge devices.

### Source excerpt

Learn how the llama.cpp runtime, GGML backend concepts, and GGUF model format fit together for fast local inference across devices.

## Introduction to ggml

DevFeed: [Introduction to ggml](<https://devfeed.tech/articles/introduction-to-ggml-7294.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/introduction-to-ggml>)

Author: Xuan-Son Nguyen; Georgi Gerganov; slaren

Published: 2024-08-13T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [ggml](<https://devfeed.tech/topics/ggml.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [compression](<https://devfeed.tech/tags/compression.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [ggml](<https://devfeed.tech/tags/ggml.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [programming](<https://devfeed.tech/tags/programming.md>), [whisper](<https://devfeed.tech/tags/whisper.md>)

### AI overview

An introductory developer tutorial on ggml, a low-level library for efficient tensor computation. It explains ggml's minimalism, compilation requirements, small binary size, hardware compatibility, quantized tensors, memory efficiency, limitations, and fundamental concepts, including how to compile it on Ubuntu.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.