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.

Local LLM Cost Savings Calculator

P-14

Model size

Quantization

GPU or unified memory

System RAM

Context target

Quality preference

Speed preference

TurboQuant KV compression

Usage notes

Live result

+$25.8/mo difference

P-14

This rough frame compares frequent API usage against a local monthly equivalent. It is for decision support, not accounting.

API estimate

$0$51.0

Recurring usage

Local equivalent

$0$25.2

Power plus amortization

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 local llm cost savings calculator.

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

Expected daily usage

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

06

Break-even framing

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

07

Useful for teams and solo builders

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 local llm cost savings calculator.

GPT-4o at 1M tok/day

Local setup~$40/month APIRunning cost

RTX 4060 Ti power

API recurring~$18/monthPower + amortization

Break-even point

Never analysed~6 monthsAt moderate usage

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 local llm cost savings calculator.

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 local llm cost savings calculator.

When does running an LLM locally actually save money?

When your usage volume justifies the hardware and electricity cost. At 1,000–5,000 requests per day, local inference typically breaks even against API costs within 2–6 months depending on GPU price and electricity rates.

What costs should I include for local inference?

Hardware amortization (GPU cost ÷ 2–3 year lifespan), electricity (~$15–25/month for a GPU running 8 hrs/day at 250W), and time cost for setup and maintenance. These are often left out of simple API vs. local comparisons.

How do I estimate my current API spend?

Check your provider billing. GPT-4o costs approximately $2.50/1M input + $10/1M output tokens. Claude 3.5 Sonnet is $3/1M input + $15/1M output. High-volume workflows accumulate significant costs fast.

Does output quality affect the cost calculation?

Yes, indirectly. If local models require more iterations or produce outputs needing manual correction, the effective cost per outcome rises. Factor quality alongside raw token costs for an honest comparison.

What usage level makes local inference clearly worth it?

If you spend more than $50–100/month on API calls, a $400–600 GPU (RTX 4060 Ti) typically pays off within 6–12 months. Daily developers and high-volume batch workflows see the clearest ROI.

What about privacy and compliance benefits?

Privacy is a non-monetary benefit with real financial value — compliance requirements, data governance, and avoiding PII exposure all have dollar costs that local inference can eliminate. Factor these in too.

RUNYARD.DEV / Tools / Local LLM Cost Savings Calculator

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