Local AI Models FAQ

A local AI model is an AI model that runs on your own computer instead of a remote cloud service. After the model is downloaded, tools like Ollama and LM Studio can load it from your local disk and generate responses using your CPU, GPU, RAM, or VRAM.

For most beginners, the easiest path is to install Ollama, open Command Prompt or Terminal, and run a small model such as llama3.2:3b. If you prefer clicking through an app instead of typing commands, LM Studio is a good alternative.

Follow our step-by-step local AI setup guide if you want exact Windows, macOS, and Linux instructions.

Start small. A 3B model is the safest first test because it downloads quickly and works on more machines. If it runs smoothly, try a 7B or 8B model for better quality.

Use the compatibility checker on the homepage to estimate whether a model fits your RAM, VRAM, or Unified Memory before downloading it.

Start with 3B if you are not sure about your hardware. Use 7B or 8B for a better balance of quality and speed on modern laptops or gaming PCs. Use 70B only if you have very large VRAM or Unified Memory.

RAM is your computer's main system memory. VRAM is dedicated GPU memory, usually found on NVIDIA or AMD graphics cards. Unified Memory, used by Apple Silicon Macs, is shared by the CPU and GPU.

For local AI, memory capacity matters because the model weights, context window, and runtime overhead all need room to load.

Yes. NVIDIA GPUs are popular because CUDA support is strong, but you can also run models on Apple Silicon, AMD GPUs, Intel GPUs, and CPUs. Performance depends heavily on memory, driver support, and the app you use.

Yes, but CPU-only generation is usually much slower than GPU generation. It can still be useful for small models, testing, offline work, and machines without a dedicated GPU.

If you use CPU only, start with a small quantized model and avoid very long context windows.

Slow generation often means the model is too large for your VRAM or Unified Memory, so it spills into slower system RAM. It can also happen with CPU-only inference, long context windows, older GPUs, or many background apps.

Try a smaller model, a more compressed quantization, or close memory-heavy apps before loading the model again.

Quantization compresses model weights so the model uses less memory. A 4-bit quantized model is much smaller than a 16-bit model, which makes it practical to run on consumer laptops and gaming PCs.

The tradeoff is that very aggressive quantization can slightly reduce answer quality, but formats like Q4 are often a good starting point.

Q4_K_M is a common 4-bit GGUF quantization format. It is popular because it usually provides a good balance between file size, memory use, speed, and answer quality.

If you are downloading a model in LM Studio, Q4_K_M.gguf is often a good first file to try.

Ollama is often fastest for beginners who are comfortable typing one command. LM Studio is better for people who prefer a ChatGPT-like graphical interface and manual control over model files.

If you want a detailed comparison, read our Ollama vs LM Studio guide.

Small 3B models can use a few gigabytes. 7B and 8B models often use about 4GB to 8GB depending on quantization. 70B models can require tens of gigabytes.

Always keep extra disk space available because apps may store model files, metadata, and temporary downloads.

Running a model locally can improve privacy because prompts are processed on your own machine instead of being sent to a cloud model API. This is useful for personal notes, private drafts, code, and sensitive documents.

However, you should still review each app's telemetry and update settings if privacy is critical.

Check the model size, quantization level, and your available RAM, VRAM, or Unified Memory. RunLocalModel estimates whether a model should fit and how comfortable the fit is.

You can also read how our estimates work to understand why the results are approximate.

Yes, but 70B models need a lot of memory. A 4-bit 70B model often needs around 40GB or more before extra context and runtime overhead. Most users should start with 3B, 7B, or 8B models first.

Restart Ollama, close memory-heavy apps, and try a smaller model. If the problem happens when loading the model, your computer probably does not have enough available memory for that model.

You can also remove unused models with ollama rm model-name to free disk space.