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.

Best Model for My Hardware Finder

P-11

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

P-11

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 best model for my hardware finder.

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

Hardware profile

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

06

Practical shortlist framing

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

07

Designed for recommendation 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 best model for my hardware finder.

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 best model for my hardware finder.

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 best model for my hardware finder.

How do I find the best model for my GPU?

Match VRAM capacity to model size + quantization VRAM. A 12 GB GPU runs 7B–13B at Q4 comfortably. A 24 GB GPU opens 32B class. Runyard Model Radar gives the full ranked shortlist for your specific hardware.

What model sizes are realistic for consumer hardware?

8 GB: up to 7B at Q4. 12 GB: up to 13B at Q4 or 7B at Q5. 24 GB: up to 32B at Q4. 48 GB: up to 70B at Q4. MoE models like DeepSeek R1 671B (37B active) run on 24 GB at Q4.

Is a bigger model always better?

No. A 7B at 30 tok/s often wins over a 70B at 2 tok/s for everyday tasks. Fit, speed, and quality are all tradeoffs. Bigger only wins when you have the hardware to run it at a comfortable speed.

How does model family affect the choice?

Different families have different strengths. Qwen 2.5 and Llama 3.1 are strong general-purpose choices. DeepSeek R1 excels at reasoning. Phi-4 punches above its weight class. CodeLlama remains solid for code generation.

Does quantization matter more than model size?

For most tasks, model size matters more. Going from Q4 to Q5 has less impact than going from 7B to 14B. But if you're at the VRAM edge, dropping to Q4 can unlock a larger model class that outweighs the quant difference.

Where can I see a live ranked list for my GPU?

Runyard Model Radar at www.runyard.dev shows all models scored by fit, estimated speed, context capacity, and quality tier for your specific hardware. That is the live answer to this question.

RUNYARD.DEV / Tools / Best Model for My Hardware Finder

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