Can I run Meta Llama 3 8B Instruct locally?

Open weights for Llama 3 8B exist and can be run locally via tools like Ollama, but this listing covers the hosted API; check Meta's release page for the weights and licence terms before downloading.

What does it cost and how long is the context window?

The API is priced at $0.03 per million input tokens and $0.04 per million output tokens, with a context window of 8,192 tokens.

How does the 8B compare to the 70B version?

Both are instruction-tuned Llama 3 models; the 70B is the stronger choice for complex reasoning and longer, more nuanced tasks, while the 8B is faster and cheaper for simpler workloads.

Meta Llama 3 8B Instruct

1991°

#llm #meta-llama #chat #writing #budget #fast

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meta-llama | 8,192 token context | $0.03/M input, $0.04/M output. Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes.

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Meta Llama 3 8B Instruct is Meta's smaller instruction-tuned model from the Llama 3 family, sitting alongside a 70B sibling. At fractions of a cent per thousand tokens it is one of the cheaper hosted options around, and the 8B size means it runs fast. A reasonable pick for everyday chat, light writing tasks and anyone watching their API bill.

Known for

Very low API cost: $0.03 per million input tokens, $0.04 output
Fast responses typical of an 8B-class model
Part of Meta's Llama 3 family, with a larger 70B sibling available
Instruction-tuned for direct question-and-answer and task completion

Watch out for

8,192-token context is short by current standards; long documents will need chunking.
Open weights exist elsewhere, but this listing does not carry them: check the licence before any commercial deployment.
The 8B size trails the 70B on complex reasoning and nuanced writing tasks.

Best for

Prompts to try

Rewrite for plain English

Rewrite the following paragraph so a non-specialist can understand it in one read. Cut jargon, keep every fact, and aim for sentences under 20 words: 'The utilization of advanced machine learning paradigms facilitates the automated extraction of latent semantic representations from unstructured corpora, enabling downstream inferential tasks with minimal human intervention.'

Draft a short email

Write a short, professional email declining a meeting request. The sender asked for 90 minutes next Tuesday to pitch a new software tool. I am not interested but want to leave the door open for a brief async summary instead. Keep it under 100 words and do not use the phrase 'I hope this email finds you well'.

Explain a concept simply

Explain how a transformer neural network decides which words in a sentence to pay attention to. Use an analogy a curious 16-year-old would follow. No bullet points, just a clear paragraph of plain prose.

Compare with

Llama 3.1 70B Instruct
Open-weights model
131,072 token context · $0.40/M input, $0.40/M output
Llama 3.3 70B Instruct Turbo
Open-weights model
131,072 token context · $0.10/M input, $0.32/M output
Meta Llama 3 70B Instruct
Open-weights model
8,192 token context · $0.30/M input, $0.40/M output
Llama 3.1 8B Instruct
Open-weights model
131,072 token context · $0.05/M input, $0.08/M output

More text models from meta-llama

Questions people ask

Can I run Meta Llama 3 8B Instruct locally?: Open weights for Llama 3 8B exist and can be run locally via tools like Ollama, but this listing covers the hosted API; check Meta's release page for the weights and licence terms before downloading.
What does it cost and how long is the context window?: The API is priced at $0.03 per million input tokens and $0.04 per million output tokens, with a context window of 8,192 tokens.
How does the 8B compare to the 70B version?: Both are instruction-tuned Llama 3 models; the 70B is the stronger choice for complex reasoning and longer, more nuanced tasks, while the 8B is faster and cheaper for simpler workloads.

Captain's verdict
A sensible budget pick for fast, simple tasks — just know the 70B exists when the job gets harder.

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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How this score adds up

This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

Heat score: One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
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Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes: Thumbs up and likes from every source, worth half a download each on the log scale.
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Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
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Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
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New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

How to install

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