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llama-cpp

Run local LLM inference with GGUF quantization across CPU and GPUs.

Updated Apr 25, 2026
Search Tags:#server#inference#llama-cpp#gguf#apple-silicon#edge-deployment#gpu-offload
One-click install
npx skills add https://github.com/danmartinez78/echo-ghost-backup --skill llama-cpp-danmartinez78
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/danmartinez78/echo-ghost-backup/tree/main/skills/mlops/inference/llama-cpp
Command: npx skills add https://github.com/danmartinez78/echo-ghost-backup --skill llama-cpp-danmartinez78

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

llama.cpp enables local, CPU- and GPU-accelerated LLM inference and quantization workflows, eliminating cloud dependencies and enabling edge deployment.

Core Features & Use Cases

  • Run LLMs locally across CPU, Apple Silicon, AMD/Intel GPUs, and NVIDIA GPUs using GGUF quantization and multiple bit-widths.
  • Provide a CLI, Python bindings, an OpenAI-compatible server, and ecosystem integrations (Ollama, LM Studio) for flexible local AI tooling.
  • Real-world use cases include offline assistants, private data processing, and rapid prototyping of local inference stacks.

Quick Start

Use llama.cpp to spin up a local inference pipeline with a supported model and test with a quick prompt.

Frequently Asked Questions about llama-cpp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run local LLM inference with GGUF quantization on Apple Silicon?▼

Run local LLM inference on Apple Silicon by deploying llama.cpp to execute GGUF quantized models across CPU and GPU layers. This setup provides fast, offline text generation and private data processing without relying on cloud infrastructure.

Can I use llama.cpp to set up an OpenAI-compatible server locally?▼

Yes, you can set up an OpenAI-compatible server locally using llama.cpp. This server deployment allows you to route local inference requests through standard API endpoints, enabling rapid prototyping and integration with existing local AI tooling.

What is the best way to convert models to GGUF format for edge deployment?▼

Converting models to GGUF format for edge deployment involves using llama.cpp quantization workflows to compress models into multiple bit-widths. This approach enables deterministic local inference and efficient GPU offload on edge devices.

Does llama.cpp support GPU offload for AMD and NVIDIA graphics cards?▼

Yes, llama.cpp supports GPU offload for AMD, Intel, and NVIDIA graphics cards. It enables hardware-accelerated LLM inference by utilizing available GPU resources alongside CPU processing, ensuring fast local execution.

How do Python bindings work with llama.cpp for local inference?▼

Python bindings for llama.cpp work by providing programmatic access to local LLM inference workflows. They allow developers to integrate GGUF model execution and GPU offload options directly into Python applications for offline assistants.

Can I use Ollama and LM Studio integrations with llama.cpp?▼

Yes, you can use Ollama and LM Studio integrations with llama.cpp. These ecosystem tooling integrations provide flexible local AI environments and simplify edge deployment workflows for GGUF quantized models.

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