The short answer: local LLM choices are hardware choices
Most local LLM questions eventually return to hardware. The model must fit in available memory, run at a usable speed, and match the task. A model that is excellent in a benchmark but uncomfortable on your computer is not a good recommendation for you. A smaller model that fully fits may be the better daily answer.
This FAQ is designed for users who are about to download a model and want plain answers first. It covers VRAM, RAM, quantization, tools, privacy, speed, offline use, and the difference between models that technically load and models that are pleasant to use.
How much VRAM do I need?
There is no single VRAM number for every local LLM. 6GB to 8GB can run small quantized models. 12GB to 16GB is more comfortable for many 7B and some 14B workflows. 24GB opens stronger local coding and reasoning models. 48GB and above provide much more room for large models, higher quantization, and longer context.
VRAM is not only for model weights. KV cache, runtime overhead, display use, and other GPU memory use also matter. Long context increases memory pressure. This is why a model may fit at a short context setting but fail or slow down when the context window is raised.
Can I run a local LLM without a GPU?
Yes, but expectations should be realistic. CPU-only local LLMs can work for small models, testing, occasional prompts, and private offline tasks. They usually feel slower than GPU-backed inference, especially for larger models or long responses. System RAM and memory bandwidth become the key limits.
If you only have CPU, choose a small model, conservative quantization, and modest context. If you plan to use a local LLM every day for coding or long conversations, a GPU or Apple Silicon system will usually feel much better.
What do Q4, Q5, Q6, and Q8 mean?
These labels describe quantized model variants. Lower quantization usually uses less memory and fits more devices. Higher quantization usually preserves more quality but needs more memory. Q4 is often the entry point, Q5 and Q6 are common daily tradeoffs, and Q8 is useful when you have enough memory and want less quality loss.
The best quantization depends on the task. Writing and chat may tolerate lower quantization better than coding or difficult reasoning. Long-context work may prefer a smaller variant so there is enough memory left for KV cache. A recommendation should show the actual variant, not only the model family.
Are local LLMs private and offline?
Local LLMs can be more private because prompts and files can stay on your machine after the model is downloaded. They can also work offline if the runtime and model files are already installed. This is useful for private notes, unpublished code, travel, and workflows where cloud access is unreliable.
Privacy is not automatic. You still need to trust the model source, check the license, avoid exposing local servers to the public internet, and understand whether any connected tool syncs logs or prompts. Local control reduces dependency on a cloud provider, but it does not remove operational responsibility.
FAQ
Which tool should I use first? LM Studio is a good graphical starting point, Ollama is a good command-line and local API starting point, and llama.cpp is powerful when you want lower-level control.
Why does my model run slowly? It may be too large, using CPU offload, running with too much context, or limited by memory bandwidth rather than raw compute.
Are downloads and likes enough to pick a model? No. They show community interest, not whether the model fits your hardware or task.
What should I do next? Use Local LLM to enter your hardware and task, then open the recommended Hugging Face page before downloading.