Science & Technology

How to Run LLMs Locally (Great For Learning and Privacy)

📚 Main Topics

Open Models and Local Deployment
- Introduction of open models like Gwen, Kimmy, and the GLM family.
- Ability to run models locally on personal hardware without needing a hosted API.
Tools for Running LLMs Locally
- Overview of five key tools for local LLM deployment.

1. llama.cpp

A C++ inference engine compatible with CPUs, GPUs, and Apple silicon.
Introduced the GGUF file format for local models, allowing for efficient model deployment.
Ideal for constrained hardware environments.

2. Ollama

A developer-friendly wrapper around llama.cpp.
Simplifies model downloads, quantization, and local server setup.
Exposes an OpenAI compatible API for easy integration.

3. LM Studio

A desktop application with a graphical interface for running LLMs.
User-friendly, allowing model browsing and downloading without terminal use.
Best suited for casual users wanting a simple chat interface.

4. vLLM

An inference engine designed for high throughput and multi-user support.
Utilizes techniques like paged attention and continuous batching to optimize performance.
Suitable for production environments, powering internal chatbots and coding assistants.

5. SGLang

An alternative to vLLM, focusing on fast serving with Radix Attention.
Efficient for workloads with shared prompt prefixes, such as multi-turn chats.

6. MLX LM

A tool by Apple for running LLMs on M-series chips.
Takes advantage of shared memory architecture for better performance on Apple devices.

✨ Key Takeaways

Local deployment of LLMs is now feasible and efficient, allowing for privacy and control over data.
Each tool serves different user needs, from quick prototyping to production-level deployment.
The choice of tool depends on the user's specific requirements, such as hardware constraints and intended use cases.

🧠 Lessons

Choosing the Right Tool
- For quick development, start with Ollama.
- For casual use, opt for LM Studio.
- For production needs, use vLLMor SGLang.
- For Apple Silicon users, MLX LMis recommended.
Advancements in Local LLMs
- The landscape of local LLM deployment has evolved significantly, making it accessible to a wider audience.
- Understanding the underlying technologies and their applications can enhance the effectiveness of AI projects.
Learning Opportunities
- Engaging in project-based learning can deepen understanding of LLMs and their applications in real-world scenarios.

Transcript excerpt

0:00 Open models like Gwen, Kimmy, and the GLM family are now a strong enough that you don't always need a hosted API. You can run them on your own laptop, so no one sees your conversation or data. Here are five tools to run LLMs locally. llama.cpp is a C++ inference engine that runs on CPUs, GPUs, and Apple silicon. It started as a side project to run llama on a MacBook and grew into the foundation most other local tools are built on.

0:32 llama.cpp also introduced a standard file format for local models, GGUF. A GGUF file packs the weights, tokenizer, and metadata into one file, and supports quantization down to 4-bit and lower, which is what makes large models fit on consumer hardware. You download a GGUF from Hugging Face, run llama.cpp and provide the model and your prompt, and you get tokens back. Use llama.cpp when you want the lightest possible

1:02 runtime, or when you are deploying to constrained hardware, like an edge device or a laptop without a dedicated GPU. Ollama is a wrapper around llama.cpp that turns it into a developer tool. It handles model downloads, quantization choices, and starting a local server so you can chat with any LLM. You run Ollama run Gemma 4, it pulls the weights, it starts a local server, and gives you a chat prompt.

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