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.

Ollama Context Length Calculator

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Model size

Quantization

GPU or unified memory

System RAM

Context target

Quality preference

Speed preference

TurboQuant KV compression

Live result

5.59 GB estimated

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Ollama Context Length Calculator puts this setup around 5.59 GB including rough runtime overhead.

Weights

-4.50 GB

Q4_K_M

KV cache

-0.29 GB

8K context

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 ollama context length 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

Model size and quantization

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

06

Expected memory overhead

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

07

Explains the hidden cost of context

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 ollama context length calculator.

Default num_ctx 2K

2 GB totalFine for quick chatOften too small

num_ctx 16K

Unknown impact+0.6 GB KVGood for coding

num_ctx 128K

Risky guess+4.8 GB KVNeeds 12+ GB free

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 ollama context length 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 ollama context length calculator.

What is num_ctx in Ollama and why does it matter?

num_ctx is the context window Ollama allocates when loading a model. A larger value reserves more KV cache. The default is often 2048, which is limiting for code review or long documents.

How do I change num_ctx in Ollama?

Set it in a Modelfile with: PARAMETER num_ctx 8192. Or use the API parameter directly. Restart the model after changing it — Ollama does not hot-reload context size changes.

What num_ctx should I use for coding tasks?

8192–16384 is a practical range for most coding workflows. Code files and conversation history together rarely exceed 8K tokens. Use 16K only if you regularly work with full files or multi-file contexts.

Will a higher num_ctx always improve responses?

Not automatically. More context helps when you have long conversations or large documents. For short tasks, it just uses more VRAM with no benefit. Set it to what the task needs, not the maximum possible.

What memory impact does doubling num_ctx have?

KV cache scales linearly with context length. Doubling from 8K to 16K roughly doubles KV cache usage. Going from 8K to 128K multiplies it by 16×. On a tight GPU, this can be the difference between fits and crashes.

What is a safe num_ctx for an 8 GB GPU with an 8B model?

For an 8B model at Q4_K_M with 8 GB VRAM, 8K–16K context is a comfortable range. At 32K you start hitting pressure. At 64K+ you will likely need TurboQuant or a lower quantization level to stay stable.

RUNYARD.DEV / Tools / Ollama Context Length Calculator

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