Runyard is a free hardware-aware AI model browser. You enter your CPU, GPU, and VRAM and it instantly shows every local LLM that will run on your machine, ranked by speed and quality.

How much VRAM do I need to run local LLMs?

8GB of VRAM runs 7B models like Llama 3.1 8B and Mistral 7B at Q4 quantization. 16GB unlocks 13B models. 24GB lets you run Mixtral 8x7B and Llama 3 70B at lower quantization.

What is the best local LLM for my GPU?

Use Runyard at www.runyard.dev — enter your GPU and VRAM and the Model Radar will rank every compatible LLM for your exact hardware, showing estimated tokens per second for each model.

Can I run Llama 3 locally?

Yes. Llama 3.1 8B at Q4 runs on any 8GB VRAM GPU. Llama 3.1 70B needs around 40GB VRAM at Q4, or an Apple Silicon Mac with 64GB+ unified memory.

Can My PC Run This AI Model?

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Model size

Quantization

GPU or unified memory

System RAM

Context target

Quality preference

Speed preference

TurboQuant KV compression

Live result

8B class is your realistic next stop

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These three pages stay lighter in the hero on purpose. The full live matching flow already exists on Runyard home.

Likely model class

Unknown8B class

12 GB VRAM

Best next action

Manual guessingOpen Model Radar

Use the main product

How It Works

3 inputs. Instant results.

01

Set the scenario

Choose realistic hardware, model, and context assumptions.

02

Read the result

The hero shows a working result instead of a decorative promo block.

03

Jump to Runyard home

The three product-led pages hand off to the main live experience.

Features

Everything that powers can my pc run this ai model?.

01

Planning-first

Built to make local-AI decisions easier to reason about.

02

Local-AI focused

Built to make local-AI decisions easier to reason about.

03

Interactive hero

Built to make local-AI decisions easier to reason about.

04

Runyard design system

Built to make local-AI decisions easier to reason about.

05

Your laptop or desktop hardware

Grounded in the actual inputs and outputs this page is designed around.

06

Compatibility framing

Grounded in the actual inputs and outputs this page is designed around.

07

Simple wording for broad search intent

Grounded in the actual inputs and outputs this page is designed around.

08

Gateway handoff

Grounded in the actual inputs and outputs this page is designed around.

Spotlight

The differentiator behind can my pc run this ai model?.

Before

GuessingInteractive resultHero section works

Reading output

Raw numbersGuided interpretationEasier next step

Product handoff

Duplicated productGateway-only heroFor the 3 requested pages

Visual comparison

Clarity

Fit

Actionability

Reading Results

How to read the output tiers.

Comfortable

<70%

Enough breathing room for normal use.

Tight

70%-95%

Should work, but overhead matters.

Borderline

95%-110%

Likely needs one tradeoff.

Too heavy

>110%

Time to step down.

Quick Reference

Common setups at useful defaults.

Scenario	Baseline	Result	Notes
Starter setup	7B / Q4 / 8K	Light local target	Good first benchmark
Balanced setup	8B / Q4 / 16K	Everyday sweet spot	Works for many users
Heavier setup	14B / Q5 / 16K	Quality-focused target	Needs stronger hardware
Stretch setup	32B / Q4 / 16K	Ambitious local target	Useful upper bound

* These are approximations for planning, not a promise of exact runtime behavior.

Benefits

Why people use can my pc run this ai model?.

01

Faster decisions

It helps eliminate dead-end local AI choices before you download, benchmark, or configure too much.

02

Clearer tradeoffs

The page turns a raw estimate into something you can actually act on.

03

Cleaner handoff to Runyard

These three pages deliberately hand off to the main product instead of pretending to replace it.

FAQ

Questions people ask before using can my pc run this ai model?.

What determines whether my PC can run an AI model?

The two main factors are VRAM (for GPU inference) and system RAM (for CPU inference). Model size and quantization determine how much you need. A 7B model at Q4_K_M needs about 4.7 GB VRAM.

What can I run with 8 GB of VRAM?

8 GB comfortably fits 7B models at Q4_K_M and most 8B models. With TurboQuant you can handle longer context. Anything above 13B generally needs 16 GB or more for comfortable inference.

Can I use system RAM instead of VRAM?

Yes, with CPU inference via Ollama or llama.cpp. Speed drops significantly — typically 5–15 tok/s for 7B on a modern CPU. It is borderline interactive for simple tasks but too slow for heavy workflows.

What about integrated graphics or laptop GPUs?

Integrated graphics share system RAM and can technically run models, but performance is poor. Laptop discrete GPUs with 4–8 GB can run small models — the same VRAM rules apply as for desktop cards.

Does system RAM speed matter for local AI?

Yes, but mainly for CPU inference. Higher-speed DDR5 or LPDDR5 improves throughput because memory bandwidth is the bottleneck. For GPU inference, VRAM bandwidth matters far more than system RAM speed.

Where do I get the exact compatibility answer for my setup?

Runyard Model Radar shows every model scored by fit, speed, and context for your specific hardware. This page explains the concept — Model Radar gives the live hardware-to-model answer.

RUNYARD.DEV / Tools / Can My PC Run This AI Model?

Estimates on this page are directional and should be validated against your actual runtime and hardware.