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Compare performance in our RTX 4070 vs 4090 comparison.
On a budget? Check our budget AI GPU guide.
For image generation, read our Stable Diffusion GPU guide.
For large models, see our best GPU for LLMs guide.
Local LLM Hardware Guide (2026)
Running LLMs locally requires more than just a powerful GPU. You need the right mix of VRAM, system RAM, storage, cooling, and expectations. This page is the starting point if you want the whole setup mapped clearly.
Step 1: pick your hardware lane
- GPU: start with the LLM VRAM guide.
- RAM: 32GB is a practical baseline for many local setups.
- Storage: use SSD storage so model handling stays sane.
Step 2: choose your software path
- Ollama for a simpler route
- LM Studio for an approachable local UI
- text-generation-webui for users who want more control
Step 3: scale smart
Many buyers should start with smaller models, then scale up after they understand real performance and memory ceilings. For parts and build guidance, see budget AI workstation builds and how to run LLMs locally.
The smartest way to size a local LLM machine
Most local-LLM buyers should start by choosing the largest model class they realistically want to run over the next year. That answer usually determines the GPU memory tier, which then shapes the rest of the build far more than smaller component differences.
After that, it is about balance: enough system RAM, fast storage, sensible cooling, and a workflow that matches whether you are experimenting casually or running models every day.
Fast sizing rules for local LLM hardware
The easiest way to size a local LLM machine is to decide what you are unwilling to compromise on. If you want the widest model flexibility, buy for VRAM headroom. If you want quiet, desk-friendly hardware, prioritize thermals and realistic sustained performance instead of peak specs.
- Entry lane: experimentation, smaller models, and learning the local stack.
- Balanced lane: smoother day-to-day local inference without overspending.
- High-headroom lane: fewer compromises on model choice and context windows.
Best upgrade path for most buyers
Most users are better off building around a stronger GPU before overpaying for top-end CPU or motherboard extras. For local LLM work, the wrong balance is common: people overspend on platform features and underspend on the part that actually decides whether the workload fits.
After this guide, use our LLM VRAM requirements page to size memory needs and the 4090 vs 4080 for AI comparison if you are split between high-end GPU tiers.