🚧 TRANSLATION PENDING - Last updated in Spanish: 2026-01-25
Local Model Ecosystems¶
🚧 TRANSLATION PENDING - Content under development
Introduction¶
This guide compares the main frameworks for running large language models (LLMs) locally, focusing on ease of use, performance, and use cases.
Main Frameworks¶
Ollama¶
- Description: Lightweight framework for running LLMs locally
- Advantages: Easy installation, integrated REST APIs
- Disadvantages: Limited to compatible models
- Use cases: Rapid development, prototyping
LM Studio¶
- Description: GUI for model management
- Advantages: Intuitive UI, wide format support
- Disadvantages: Less integration-oriented
- Use cases: End users, interactive testing
LLaMA.cpp¶
- Description: Efficient C++ implementation of LLaMA
- Advantages: High performance, low resource consumption
- Disadvantages: Requires compilation, less beginner-friendly
- Use cases: Production, limited hardware
vLLM¶
- Description: Framework for LLM inference at scale
- Advantages: Tensor parallelism, high throughput
- Disadvantages: Complex to configure
- Use cases: Enterprise deployment
Technical Comparison¶
| Framework | Language | GPU Support | API | Ease |
|---|---|---|---|---|
| Ollama | Go | Yes | REST | High |
| LM Studio | C++ | Yes | Local | High |
| LLaMA.cpp | C++ | Yes | CLI | Medium |
| vLLM | Python | Yes | HTTP | Low |
Installation and Configuration¶
Ollama¶
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull llama2
LM Studio¶
Download from https://lmstudio.ai/
LLaMA.cpp¶
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
Practical Cases¶
- Local chatbots: Use Ollama with Streamlit
- Code analysis: Integration with VS Code
- Offline processing: LLaMA.cpp on edge devices