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How to Run LLMs Locally (2026)

AI hardware research context

This guide is part of our AI hardware research covering GPU performance, VRAM requirements, and real-world workloads like Stable Diffusion and local LLM inference.

Reviewed by the GrokTech Editorial Team using our published methodology. No paid placements.

Build a realistic local LLM setup

Before you install anything, check the site’s VRAM requirements for AI and best GPU for LLM inference. If you need a portable machine instead of a desktop, use the best AI laptops guide.

By GrokTech Editorial Team

Reviewed against our published methodology for AI hardware fit, thermal limits, upgrade tradeoffs, and real-world workload suitability. Updated monthly or when market positioning changes.

Running LLMs locally gives you more control, privacy, and flexibility—but only if your hardware and setup are right. This guide walks through the whole process from beginner to power-user level.

Step 1: hardware requirements

Start with GPU VRAM and use our LLM VRAM requirements guide.
Use 32GB RAM as a comfortable baseline.
Use SSD storage.

Step 2: software setup

Ollama
LM Studio
text-generation-webui

Step 3: optimization tips

Use quantization when needed.
Keep expectations aligned to VRAM.
Scale up only after you know your actual workload.

A simple setup order that avoids headaches

The cleanest way to start is to pick the model size you actually want, confirm that your GPU memory tier fits it, then choose one straightforward tool such as Ollama or LM Studio for your first run. That order prevents a lot of unnecessary troubleshooting.

Once the basics work, then it makes sense to optimize with quantization choices, different front ends, or a more advanced stack. Readers who try to solve everything at once usually make local AI harder than it needs to be.

Hardware pages worth opening next

Start with the right expectations

Running LLMs locally is easiest when you match the model size to your available VRAM. Smaller models feel far more responsive on mainstream hardware, while larger models quickly expose memory limits and slowdowns.

What usually becomes the bottleneck

For most local setups, VRAM is the first hard limit. Once a model spills beyond GPU memory, performance drops sharply, which is why GPU choice matters more than raw CPU speed for many local LLM workflows.

What to decide before you start

Whether you care more about low cost, model size, or long-term upgrade flexibility
How much VRAM your target models need
Whether a laptop, desktop, or prebuilt system fits your space and workflow better

Making those choices early simplifies everything that follows, from GPU selection to overall budget planning.

Best next pages to read

Use LLM VRAM requirements to understand model memory needs, best GPU for LLM inference to pick a hardware tier, and can you run an LLM on 8GB VRAM? if you are deciding whether an entry setup is enough.