Run Local AI Models on Your PC

By the RunLocalModel editorial team · Last updated: May 6, 2026

Welcome to RunLocalModel.com - a free, independent compatibility checker for local large language models. Pick your GPU (or input your VRAM and memory bandwidth manually), pick a model, and we will tell you whether it fits, what context length you can afford, and a realistic tokens-per-second estimate. The whole tool runs in your browser, requires no account, and never uploads your hardware information anywhere.

The compatibility checker is loading now. If you only see this page, your browser may be blocking JavaScript - the calculator itself is a small React app that runs entirely client-side. While it loads, the rest of this page is a quick orientation to what you can do with the site.

How the compatibility checker works

For every model in our database we estimate three things:

• Required VRAM = quantized weight size + KV cache for your context length + a small fixed overhead. The KV cache term is what makes long-context models so memory-hungry, and the checker lets you compare F16, Q8, and Q4 KV cache settings side-by-side.
• Compatibility grade from "Runs great" down to "Too heavy", based on how much of your VRAM the configuration would actually use. We bias toward leaving headroom rather than squeezing every megabyte.
• Tokens per second, derived from your GPU's effective memory bandwidth divided by the on-disk model size, with a vendor-specific efficiency factor and a long-context penalty.

The exact formulas, the assumptions behind them, and the caveats are documented openly on our How It Works page.

Why run AI models locally?

Running an LLM on your own hardware is no longer a niche hobby in 2026. Compared to a hosted API or chat product, the local approach gives you:

• Privacy. Prompts, code, documents, and any sensitive context never leave your machine. There is no provider account, no log retention, no terms-of-service surprise about training on your data.
• Predictable cost. One-time hardware cost, then unlimited use. No per-token billing, no surprise bills from a runaway agent loop.
• Offline availability. The model works on a flight, in a cafe with bad wifi, or in environments where outbound network calls are not allowed.
• Full control. You pick the model family, the quantization, the context length, the system prompt, the sampling parameters - and you can keep using a model long after a vendor decides to deprecate it.

Where to start, by hardware tier

A rough overview of what we recommend for the most common consumer hardware in 2026. The site's checker will give you a more specific answer for the exact model you have in mind:

• 8 GB VRAM laptops and entry desktops (RTX 3050, 4060, M-series base): a 7B-8B model at Q4_K_M. See our 2026 Model Guide for specific picks.
• 12 GB VRAM (RTX 3060 12GB, 4070): the sweet spot. 7B-8B at Q5_K_M, or a 12B-14B at Q4_K_M with a comfortable context window.
• 16-24 GB VRAM (RTX 4070 Ti Super, 3090, 4090, 16GB Apple Silicon): 14B-32B models at Q4_K_M-Q5_K_M. This is where local feels indistinguishable from hosted for most chat and coding tasks.
• 48 GB+ workstations or 64GB+ Apple Silicon: 70B at Q4_K_M or Q5_K_M with a long context.

For a deeper discussion of what each quantization actually costs you in quality and what to pick when you are 1-2 GB short, read our long-form guide: Choosing the Right Quantization for Local LLMs in 2026.

Picking a runtime

The checker is engine-agnostic - the underlying memory math is the same whether you use Ollama, LM Studio, Jan, or raw llama.cpp. For most people new to local LLMs in 2026 we recommend starting with one of the first two. We compare them side by side in Ollama vs LM Studio, and walk through end-to-end setup in How to Run Local AI.