Uploaded July 2026 | Updated September 2026, 3 weeks ago
🦙 llama-server Command Line Overview | Complete Guide to Every Option in llama.cpp
Want to host Large Language Models locally with llama-server? In this comprehensive tutorial, we explore the llama-server command line included with llama.cpp, explaining the most important options and how to configure them for the best performance.
Whether you're running AI models on a CPU, GPU, or creating your own OpenAI-compatible API server, this guide will help you understand the available command-line arguments and how they affect your local LLM.
🚀 In This Video You'll Learn
✅ What is llama-server?
✅ How llama-server differs from llama-cli
✅ Starting a local LLM server from the command line
✅ Loading GGUF models
✅ GPU acceleration options (-ngl)
✅ Context size (-c) explained
✅ Threads and CPU optimization
✅ Host and port configuration
✅ OpenAI-compatible API endpoints
✅ Performance tuning and memory settings
✅ Logging and debugging options
✅ Practical command examples
By the end of this tutorial, you'll know how to configure llama-server for development, testing, and production use.
📚 Topics Covered
llama-server
llama.cpp
llama-server command line
Local AI server
GGUF models
OpenAI compatible API
Local LLM hosting
GPU acceleration
CPU inference
Context window
Threads optimization
AI server configuration
Self-hosted AI
Offline AI
Large Language Models (LLMs)
⏱️ Chapters
- Introduction
- What is llama-server?
- Basic Command Syntax
- Loading GGUF Models
- GPU Options (-ngl)
- Context Size (-c)
- Threads and Performance
- Network Options (Host & Port)
- API Endpoints
- Advanced Options
- Best Practices
- Conclusion
💡 Commands used in this video:
PS C:\llama.cpp.\llama-server
-m, --model FNAME model path (default: models/7B/ggml-model-f16.gguf)
--host HOST ip address to listen (default: 127.0.0.1)
--port PORT port to listen (default: 8080)
-c, --ctx-size N size of the prompt context (default: 4096)
-n, --n-predict N number of tokens to predict (default: -1, unlimited)
--temp N temperature (default: 0.8)
-ngl, --n-gpu-layers N number of layers to store in VRAM
-fa, --flash-attn enable Flash Attention
-t, --threads N number of threads to use during generation
💡 If you're learning about local AI, llama.cpp, GGUF models, or self-hosting Large Language Models, don't forget to Like, Subscribe, and enable notifications for more in-depth tutorials.
🌸 Buy me a coffee :
buymeacoffee.com/liv4it
🌸 Support channel & make donation :
paypal.me/aminenina/10
🌸 Subscribe for more videos :
Youtube: youtube.com/user/aminosninatos
🌸 Follow me On Social Media
Facebook : facebook.com/aminosninatos
***********************************************************************
🌸 🦙 Run Large Language Models on Your Computer Using llama.cpp | Complete Beginner's Guide
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***********************************************************************
#llamacpp #llamaserver #LLM
🦙 llama-server Command Line Overview | Complete Guide to Every Option in llama.cpp
Want to host Large Language Models locally with llama-server? In this comprehensive tutorial, we explore the llama-server command line included with llama.cpp, explaining the most important options and how to configure them for the best performance.
Whether you're running AI models on a CPU, GPU, or creating your own OpenAI-compatible API server, this guide will help you understand the available command-line arguments and how they affect your local LLM.
🚀 In This Video You'll Learn
✅ What is llama-server?
✅ How llama-server differs from llama-cli
✅ Starting a local LLM server from the command line
✅ Loading GGUF models
✅ GPU acceleration options (-ngl)
✅ Context size (-c) explained
✅ Threads and CPU optimization
✅ Host and port configuration
✅ OpenAI-compatible API endpoints
✅ Performance tuning and memory settings
✅ Logging and debugging options
✅ Practical command examples
By the end of this tutorial, you'll know how to configure llama-server for development, testing, and production use.
📚 Topics Covered
llama-server
llama.cpp
llama-server command line
Local AI server
GGUF models
OpenAI compatible API
Local LLM hosting
GPU acceleration
CPU inference
Context window
Threads optimization
AI server configuration
Self-hosted AI
Offline AI
Large Language Models (LLMs)
⏱️ Chapters
- Introduction
- What is llama-server?
- Basic Command Syntax
- Loading GGUF Models
- GPU Options (-ngl)
- Context Size (-c)
- Threads and Performance
- Network Options (Host & Port)
- API Endpoints
- Advanced Options
- Best Practices
- Conclusion
💡 Commands used in this video:
PS C:\llama.cpp.\llama-server
-m, --model FNAME model path (default: models/7B/ggml-model-f16.gguf)
--host HOST ip address to listen (default: 127.0.0.1)
--port PORT port to listen (default: 8080)
-c, --ctx-size N size of the prompt context (default: 4096)
-n, --n-predict N number of tokens to predict (default: -1, unlimited)
--temp N temperature (default: 0.8)
-ngl, --n-gpu-layers N number of layers to store in VRAM
-fa, --flash-attn enable Flash Attention
-t, --threads N number of threads to use during generation
💡 If you're learning about local AI, llama.cpp, GGUF models, or self-hosting Large Language Models, don't forget to Like, Subscribe, and enable notifications for more in-depth tutorials.
🌸 Buy me a coffee :
buymeacoffee.com/liv4it
🌸 Support channel & make donation :
paypal.me/aminenina/10
🌸 Subscribe for more videos :
Youtube: youtube.com/user/aminosninatos
🌸 Follow me On Social Media
Facebook : facebook.com/aminosninatos
***********************************************************************
🌸 🦙 Run Large Language Models on Your Computer Using llama.cpp | Complete Beginner's Guide
youtu.be/up-iIZNj3mw
🌸 Download Hugging Face Models FAST! 🚀 HF CLI + Xet Storage Engine (Complete 2026 Guide)
youtu.be/8uDbEy2VKKs
🌸📦 Backblaze B2 Cloud Storage Rclone Log Every File Transfer & Verify Your Backups (Complete Guide)
youtu.be/AcLU4xdoj0U
🌸📦Backblaze B2 Cloud Storage & Rclone Useful Commands for Troubleshooting
youtu.be/rsGrBQz7U80
🌸📦 Backblaze B2 Cloud Storage What Happens When the Connection Drops Mid-Transfer
youtu.be/6vdMXjj_QNU
🌸📦 Backblaze B2 Cloud Storage & Rclone on Windows — Complete Setup & Sync Guide
youtu.be/5z_70VYiGp8
🌸🛡️ Deploy Wazuh XDR & SIEM with Docker — Full Step-by-Step Guide
youtu.be/5oqM1niLUc4
🌸🚀 🔥 FortiGate 8.0 CLI Commands Tutorial | Complete Guide for Beginners & Network Admins
youtu.be/4ZWLJ_mZ8us
🌸🚀 How to perform the basic initial configuration of a FortiGate 8.0 VM running on VMware ESXi
youtu.be/ae9oOmbWzPA
🌸🔥How To Deploy FortiGate 8.0 on ESXi Host & Activate Trial License (Step-by-Step Guide)🔥
youtu.be/BKBq_XzUj0U
🌸🚀 FortiOS 8.0 — What's New AI Security, Quantum Safe VPN & SASE Explained Simply 🔥
youtu.be/bcOOp3C9oWc
🌸🔥 Learn how to configure Port Forwarding on MikroTik Router (RouterOS) – Step-by-Step Guide 🔥
youtu.be/jJtcKccnCAY
🌸🔐 How to Configure WireGuard VPN on MikroTik Router — Step-by-Step Tutorial (RouterOS 7)
youtu.be/zjl8Dn0p1I0
🌸 📡 How to Set Up Telegram Notifications on a MikroTik Router | Step-by-Step Guide
youtu.be/ZdMNAIyAFdE
🌸 🚀 Automate MikroTik Router Configuration with Terraform | Infrastructure as Code Tutorial
youtu.be/MoJmPc-HV4Y
🌸 🔐How to Set Up SSH Key Authentication on MikroTik Router
youtu.be/Ks0YcPUclWc
🌸 📌Mikrotik BGP Peering with DN42 Network
youtu.be/hHDcGfjJH0I
🌸 📌 MikroTik RouterOS Scripting
youtu.be/EsoWYQH6Gk0
🌸 🔐I Asked AI to Fix My MikroTik Firewall – Here’s What Happened
youtu.be/RbI-X0ZXXbg
***********************************************************************
#llamacpp #llamaserver #LLM
