What are the minimum hardware requirements for running local LLMs?

For basic usage, you need at least 16GB RAM, 50GB storage, and preferably a GPU with 8GB+ VRAM. Entry-level models (7-8B parameters) can run on GPUs like RTX 3060 with 8GB VRAM, while larger models require 16-24GB VRAM or more. CPU-only operation is possible but significantly slower.

Which framework should I choose as a beginner?

Ollama is currently the most beginner-friendly framework, offering a Docker-like experience with simple commands for installation and model management. LM Studio is another excellent choice for beginners, providing a graphical interface and intuitive model management.

How much can I save by running LLMs locally?

Organizations can save 80-90% on costs compared to cloud APIs, particularly for high-volume applications. While there's an initial hardware investment, local deployment eliminates per-token charges, making it highly cost-effective for continuous usage.

Can I run large models like 70B parameter LLMs locally?

Yes, but they require significant hardware resources. A 70B model typically needs 48GB+ VRAM even with 4-bit quantization. Multi-GPU setups or specialized hardware may be necessary. Alternatively, smaller 7-14B models offer good performance with more modest hardware requirements.

What's the best quantization method for local LLMs?

4-bit quantization (Q4_K_M) typically provides the best balance between model size and performance in 2025. It reduces VRAM requirements by 75% with minimal quality loss. For ARM architectures, the new Q4_0_4_8 quantization offers additional optimization.

How do I choose the right model for my needs?

Consider your hardware capabilities, use case, and performance requirements. For general-purpose tasks, 7-8B models like Llama 3.1 8B or Qwen 2.5 7B work well on consumer hardware. For advanced applications, larger models like DeepSeek R1 32B or Llama 3.1 70B offer superior capabilities but require more powerful hardware.

Can I run local LLMs completely offline?

Yes, once models are downloaded, all major frameworks support completely offline operation. This makes local LLMs ideal for privacy-sensitive applications or environments without internet access.

What about API compatibility with existing applications?

Many frameworks now offer OpenAI-compatible APIs, allowing you to migrate existing applications to local deployment with minimal code changes. Tools like vLLM and LocalAI provide drop-in replacements for OpenAI's API endpoints.

How to Run a Local LLM: A Comprehensive Guide for 2025

Interactive Content

Tutorials

Published on August 14, 2025

Back to Blog More in Tutorials