Can I run Meta Llama 3.1 8B Instruct Turbo locally?

Not through this listing — the weights are not published here, so access is API-only. The base Llama 3.1 8B weights are available elsewhere for local use, but this Turbo variant is a hosted, speed-optimised serving configuration.

What does it cost and how long is the context?

The API is priced at $0.02 per million input tokens and $0.04 per million output tokens, with a 131,072-token context window.

How does the 8B differ from the 70B and 405B in the same family?

All three share the same Llama 3.1 architecture and context length, but the 8B is faster and cheaper at the cost of weaker reasoning and instruction-following; the 70B and 405B are the right choice when output quality matters more than speed or price.

Meta Llama 3.1 8B Instruct Turbo

2249°

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

Open DeepInfra

meta-llama | 131,072 token context | $0.02/M input, $0.04/M output. Meta developed and released the Meta Llama 3.1 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Meta Llama 3.1 8B Instruct Turbo is the smallest and fastest member of Meta's Llama 3.1 family, served via API at near-negligible cost. It handles a 131,072-token context window, which is generous for its size class. If you need quick, cheap text generation and can accept the trade-offs of an 8B model, this is a practical workhorse.

Known for

Extremely low API cost: $0.02 per million input tokens, $0.04 per million output.
131,072-token context window, unusually large for a model this small.
Part of a family spanning 8B, 70B and 405B, so scaling up is straightforward.
Turbo serving optimised for speed over raw capability.

Watch out for

Weights are not open on this listing, so you are tied to the API provider.
An 8B model will hit reasoning and instruction-following limits that the 70B and 405B siblings handle better.
Meta has not published the licence terms here; check before any commercial deployment.

Best for

Prompts to try

Summarise a long document

Here is a long article. Give me a five-bullet executive summary, then a one-paragraph plain-English explanation suitable for someone who has never worked in this industry. Keep both sections under 200 words total. [Paste your article here]

Draft a quick reply

I received this email from a client asking why their order is delayed. Write a professional but warm reply in under 100 words. Acknowledge the frustration, give a brief honest reason (supply chain issue), and offer a 10% discount on their next order. [Paste the email here]

Extract structured data

Read the following block of unstructured text and extract every person's name, their job title, and the company they work for. Return the results as a JSON array with keys: name, title, company. If a field is missing, use null. Sarah Chen, VP of Engineering at Northgate Systems, met with Tom Okafor, a freelance consultant, and Dr. Priya Nair from Helios Biotech.

Compare with

Llama 3.2 3B Instruct
Open-weights model
131,072 token context · $0.05/M input, $0.33/M output
Llama 3.1 8B Instruct
Open-weights model
131,072 token context · $0.05/M input, $0.08/M output
Llama 3.2 1B Instruct
Open-weights model
60,000 token context · $0.03/M input, $0.20/M output
Meta Llama 3 8B Instruct
Open-weights model
8,192 token context · $0.03/M input, $0.04/M output

More text models from meta-llama

Questions people ask

Can I run Meta Llama 3.1 8B Instruct Turbo locally?: Not through this listing — the weights are not published here, so access is API-only. The base Llama 3.1 8B weights are available elsewhere for local use, but this Turbo variant is a hosted, speed-optimised serving configuration.
What does it cost and how long is the context?: The API is priced at $0.02 per million input tokens and $0.04 per million output tokens, with a 131,072-token context window.
How does the 8B differ from the 70B and 405B in the same family?: All three share the same Llama 3.1 architecture and context length, but the 8B is faster and cheaper at the cost of weaker reasoning and instruction-following; the 70B and 405B are the right choice when output quality matters more than speed or price.

Captain's verdict
At two cents per million tokens and a 128k context, this is the model you reach for when volume and speed matter more than depth.

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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Updated Oct 22, 2026

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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.
Floor: The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
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.
Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
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.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
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.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
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

How to Install Ollama and Run a Local AI (Windows, Mac, Linux)

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