This content originally appeared on Level Up Coding - Medium and was authored by Huda Saleh
Introduction
llama.cpp is an implementation of LLM inference code written in pure C/C++, deliberately avoiding external dependencies. The core philosophy prioritizes:
- Strict memory management and efficient multi-threading
- Minimal dependencies for maximum portability
- Low-level resource control for optimal performance
This C++-first methodology enables llama.cpp to run on an exceptionally wide array of hardware, from high-end servers to resource-constrained edge devices like Android phones and Raspberry Pis.
Core Value Proposition
llama.cpp delivers different essential attributes that set it apart:
1. Performance: Through aggressive low-level optimizations and quantization techniques, llama.cpp achieves strong inference throughput even on hardware not originally designed for machine learning.
2. Portability: The self-contained C/C++ codebase compiles and runs on virtually any modern architecture:
- x86 and ARM (especially Apple Silicon)
- Windows, macOS, and Linux
- Edge devices and consumer hardware
3. Accessibility: By minimizing dependencies, llama.cpp simplifies setup and makes powerful AI tools accessible to developers, hobbyists, and researchers without enterprise-grade infrastructure.
Why Choose llama.cpp?
While there are other inference solutions like vLLM and Ollama, llama.cpp excels specifically for:
- Local inference on personal hardware
- Resource-constrained environments with limited compute
- Full control over the inference process
- No cloud dependencies for privacy-sensitive applications
Installation and Setup (Windows)
Prerequisites
Install Visual Studio Community https://visualstudio.microsoft.com/vs/community/
- Download from the official Microsoft website
- During installation, select Desktop development with C++
- Ensure CMake is selected in the installation options
Add CMake to Environment Variables
Add the following paths to your system’s PATH environment variable (adjust version numbers as needed):
C:\Program Files\Microsoft Visual Studio\20XX\Community\VC\Tools\MSVC\xxxx\bin\Hostx64\x64
C:\Program Files\Microsoft Visual Studio\20XX\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin
Building llama.cpp
Clone the Repository
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
Build with CMake
For CPU-only inference:
cmake -B build -DLLAMA_CURL=OFF -DUSE_CURL=ON
cmake --build build --config Release
For GPU acceleration (CUDA):
cmake -B build -DLLAMA_CUBLAS=ON
cmake --build build --config Release
For GPU acceleration (OpenCL):
cmake -B build -DLLAMA_CLBLAST=ON
cmake --build build --config Release
The executables will be located in ./build/bin/Release/
Downloading Models
Before running llama.cpp, you need to download GGUF format models. There are two approaches:
Option 1: Manual Download from Hugging Face
Visit Hugging Face and search for GGUF models (e.g., search for “gemma GGUF” or “llama GGUF”)
Download the GGUF file (e.g.,https://huggingface.co/unsloth/gemma-3-4b-it-GGUF/tree/main gemma-3-4b-it-Q4_0.gguf)
Create a models directory in your llama.cpp folder:
# Navigate back to llama.cpp root directory
cd ../../..
mkdir custom-models
Place the downloaded GGUF file in the custom-models folder in llama.cpp folder
For vision models, you need two files:
- The main model file (e.g., MiniCPM-V-4_5-F16.gguf)
- The multimodal projector file (e.g., mmproj-model-f16.gguf)
Option 2: Automatic Download with Hugging Face CLI
llama.cpp can automatically download models from Hugging Face using the -hf flag:
bash
cd ./build/bin/Release/
# Download and run a model directly from Hugging Face
llama-cli.exe -hf unsloth/gemma-3-4b-it-GGUF -m gemma-3-4b-it-Q4_0.gguf
# For server mode with automatic download
llama-server.exe -hf bartowski/Llama-3.2-3B-Instruct-GGUF \
-m Llama-3.2-3B-Instruct-Q4_K_M.gguf \
--host 127.0.0.1 \
--port 8080
Using llama.cpp
CLI Mode for Text Generation
Run text inference with:
# Basic usage
llama-cli.exe -m ../../../custom-models/gemma-3-4b-it-Q4_0.gguf
# With additional parameters
llama-cli.exe -m ../../../custom-models/gemma-3-4b-it-Q4_0.gguf \
-p "Explain quantum computing in simple terms" \
-n 512 \
--temp 0.7 \
--top-p 0.9 \
-c 4096
CLI Mode for Vision Models (Multimodal)
For image understanding with vision models:
llama-mmproj-cli.exe -m ../../../custom_models/MiniCPM-V-4_5-F16.gguf \
--mmproj ../../../custom_models/mmproj-model-f16.gguf \
-c 4096 \
--temp 0.3 \
-p "Describe this image in detail." \
--image ../../../media/llama-banner.png
Server Mode
Launch an OpenAI-compatible API server:
Text Model Server (CPU Only):
llama-server.exe -m ../../../custom-models/gemma-3-4b-it-Q4_0.gguf \
--host 127.0.0.1 \
--port 8080 \
--n-gpu-layers 0 \
-c 4096 \
--threads 8
Text Model Server (With GPU Acceleration):
llama-server.exe -m ../../../custom-models/gemma-3-4b-it-Q4_0.gguf \
--host 127.0.0.1 \
--port 8080 \
--n-gpu-layers 35 \
-c 4096 \
--threads 8
Vision Model Server:
llama-server.exe -m ../../../custom_models/MiniCPM-V-4_5-F16.gguf \
--mmproj ../../../custom_models/mmproj-model-f16.gguf \
-c 4096 \
--temp 0.3 \
--port 8080 \
--host 0.0.0.0 \
--jinja \
--n-gpu-layers 20
The server provides an OpenAI-compatible API endpoint at http://127.0.0.1:8080/v1, making it compatible with any tool or framework that supports the OpenAI API format.
For vision models with GPU:
llama-server.exe -m ../../../custom_models/MiniCPM-V-4_5-F16.gguf \
--mmproj ../../../custom_models/mmproj-model-f16.gguf \
--host 127.0.0.1 \
--port 8080 \
--n-gpu-layers 25 \
-c 4096 \
--temp 0.3 \
--jinja
Integration with Agentic Frameworks
OpenAI API Compatibility
The llama.cpp server implements the OpenAI API specification, allowing seamless integration with popular AI frameworks. You can use it as a drop-in replacement for OpenAI’s API by:
- Setting the base_url to your local llama.cpp server
- Using any placeholder API key (not validated locally)
- Specifying your model path in the model parameter
LangChain Integration
import os
from langchain_openai import ChatOpenAI
# Set a dummy API key
os.environ["OPENAI_API_KEY"] = "localdummykey"
# Initialize LangChain with local llama.cpp server
llm = ChatOpenAI(
model="custom-models/gemma-3-4b-it-Q4_0.gguf",
temperature=0.6,
base_url="http://127.0.0.1:8080/v1",
api_key="localdummykey"
)
# Use the model
response = llm.invoke("What are the benefits of local LLM inference?")
print(response)
CrewAI Integration
CrewAI enables multi-agent collaboration with structured outputs. Here’s a complete example that generates an AI trends report:
import os
from crewai import Agent, Task, Crew, Process
from crewai.llm import LLM
from pydantic import BaseModel, Field
from typing import List
# Define structured output schema
class AITrendsReport(BaseModel):
trends: List[str] = Field(description="List of key AI trends identified")
implications: List[str] = Field(description="Implications for industries")
recommendations: List[str] = Field(description="Actionable recommendations")
# Set dummy API key
os.environ["OPENAI_API_KEY"] = "localdummykey"
# Initialize LLM
llm = LLM(
model="openai/custom-models/gemma-3-4b-it-Q4_0.gguf",
base_url="http://127.0.0.1:8080/v1",
api_key="localdummykey",
temperature=0.6,
)
# Test connection
try:
response = llm.invoke("Hello")
print("LLM Response:", response)
except Exception as e:
print("Error connecting to LLM:", e)
# Define agents
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover groundbreaking insights from various data sources',
backstory='A seasoned analyst with a knack for detail and identifying emerging trends.',
llm=llm,
verbose=True
)
data_scientist = Agent(
role='Data Scientist',
goal='Analyze technical aspects of AI trends and provide data-driven insights',
backstory='An expert in machine learning adept at extracting meaningful patterns.',
llm=llm,
verbose=True
)
tech_writer = Agent(
role='Tech Writer',
goal='Craft clear, concise, and engaging reports based on technical insights',
backstory='A skilled communicator specializing in translating complex concepts.',
llm=llm,
verbose=True
)
# Define tasks
research_task = Task(
description='Identify and summarize the latest trends in AI.',
expected_output='A detailed list of current AI trends with brief descriptions.',
agent=researcher,
output_pydantic=AITrendsReport
)
analysis_task = Task(
description='Analyze the identified AI trends and their implications.',
expected_output='Detailed analysis of implications for various sectors.',
agent=data_scientist,
context=[research_task],
output_pydantic=AITrendsReport
)
writing_task = Task(
description='Compile research and analysis into a comprehensive report.',
expected_output='A polished AI trends report in structured JSON format.',
agent=tech_writer,
context=[research_task, analysis_task],
output_pydantic=AITrendsReport
)
# Create and run the crew
crew = Crew(
agents=[researcher, data_scientist, tech_writer],
tasks=[research_task, analysis_task, writing_task],
process=Process.sequential,
verbose=True
)
try:
result = crew.kickoff()
print("CrewAI Result:", result)
if isinstance(result, AITrendsReport):
print("Structured Report:", result.model_dump_json(indent=2))
except Exception as e:
print("CrewAI Error:", e)
Pydantic AI Integration
For vision models with structured outputs:
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic import BaseModel
from pydantic_ai.providers.openai import OpenAIProvider
from pydantic_ai import Agent
class ImageAnalysisResult(BaseModel):
"""Result from image analysis"""
description: str
# Initialize model with local llama.cpp server
model = OpenAIChatModel(
'openai/custom_models/MiniCPM-V-4_5-F16.gguf',
provider=OpenAIProvider(
base_url='http://127.0.0.1:8080/v1',
api_key='sk-no-key'
)
)
# Create agent with structured output
agent = Agent(
model,
output_type=ImageAnalysisResult,
instructions="You are an expert image analysis assistant. Analyze images and provide detailed descriptions."
)
# Use the agent (add your image processing logic here)
Key Takeaways
- llama.cpp democratizes LLM access by enabling local inference on consumer hardware
- OpenAI API compatibility makes integration with existing tools seamless
- Quantization techniques allow powerful models to run on limited resources
- Agentic frameworks like CrewAI and LangChain work out-of-the-box with llama.cpp
- Vision models are supported for multimodal applications
Conclusion
llama.cpp has fundamentally changed how we interact with Large Language Models, making them accessible to anyone with a personal computer. Whether you’re building AI agents, experimenting with local inference, or developing privacy-focused applications, llama.cpp provides the performance and flexibility you need.
Complete Guide to llama.cpp: Local LLM Inference Made Simple was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by Huda Saleh
Huda Saleh | Sciencx (2025-10-22T21:58:26+00:00) Complete Guide to llama.cpp: Local LLM Inference Made Simple. Retrieved from https://www.scien.cx/2025/10/22/complete-guide-to-llama-cpp-local-llm-inference-made-simple/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.