Question 1

How much RAM do I need to run a 7B parameter model locally?

Accepted Answer

To run a 7B parameter model (like Llama 3 or Mistral) with 4-bit quantization, you typically need at least 8GB of RAM. However, for a smooth experience and to handle longer contexts, 16GB of Unified Memory (on Mac) or VRAM (on NVIDIA) is recommended.

Question 2

Can I run LLMs on a MacBook Air with M2 or M3 chips?

Accepted Answer

Yes, Apple Silicon (M1, M2, M3) is excellent for local LLMs due to its Unified Memory Architecture. A MacBook Air with 16GB of RAM can comfortably run 7B and 8B models. For 70B models, you would need a MacBook Pro with at least 64GB or 96GB of Unified Memory.

Question 3

What is the best local AI model for coding in 2024?

Accepted Answer

Currently, models like CodeLlama, DeepSeek-Coder, and StarCoder2 are highly rated for local deployment. Your choice depends on your hardware; use runlocalmodel.com to find which coding assistant fits your specific GPU/RAM setup.

Question 4

Do I need an NVIDIA GPU to run local AI models?

Accepted Answer

While NVIDIA GPUs with CUDA cores are the industry standard, you can also run models on Apple Silicon (using MLX or Metal) and even on CPUs using llama.cpp. However, performance on a CPU will be significantly slower compared to a dedicated GPU or Unified Memory.

Local LLM & Hardware FAQs