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 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.

OOM Fix Assistant | Runyard Tools

P-15

Model size

Quantization

GPU or unified memory

System RAM

Context target

Quality preference

Speed preference

TurboQuant KV compression

Live result

Likely headroom issue

P-15

OOM failures usually come from the same few causes: weights too large, context too long, or a setup that is simply too tight.

Current ask

12 GB5.59 GB

Estimated requirement

Fastest fix

Random trial and errorReduce context

Highest-probability move

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

Act on the outcome

Use the result to adjust fit, speed, quantization, or context.

Features

Everything that powers oom fix assistant.

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

The error pattern you saw

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

06

Likely root cause

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

07

Good for debugging under pressure

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

08

Standalone tool

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

Spotlight

The differentiator behind oom fix assistant.

Before

GuessingInteractive resultHero section works

Reading output

Raw numbersGuided interpretationEasier next step

Product handoff

Duplicated productStandalone tool heroFor the actual tools

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 oom fix assistant.

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

Useful on its own

The hero provides a working tool surface while the rest of the page explains what the output means.

FAQ

Questions people ask before using oom fix assistant.

What does oom fix assistant estimate?

This page is built to answer the question in a planning-friendly way for local AI users. The hero gives the immediate result, and the rest of the page explains how to interpret it.

Are these numbers exact?

This page is built to answer the question in a planning-friendly way for local AI users. The hero gives the immediate result, and the rest of the page explains how to interpret it.

Why use this before testing manually?

This page is built to answer the question in a planning-friendly way for local AI users. The hero gives the immediate result, and the rest of the page explains how to interpret it.

Does this apply to Ollama and GGUF workflows?

This page is built to answer the question in a planning-friendly way for local AI users. The hero gives the immediate result, and the rest of the page explains how to interpret it.

How should I pick my inputs?

This page is built to answer the question in a planning-friendly way for local AI users. The hero gives the immediate result, and the rest of the page explains how to interpret it.

When should I use Runyard home instead?

This page is built to answer the question in a planning-friendly way for local AI users. The hero gives the immediate result, and the rest of the page explains how to interpret it.

RUNYARD.DEV / Tools / OOM Fix Assistant

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