Can I run Gemma 3 27B locally?

Yes, the weights are open and available on Hugging Face at google/gemma-3-27b-it. At 27 billion parameters you will want at least 24 GB of VRAM for comfortable inference; quantised GGUF versions reduce that requirement.

What does Gemma 3 27B cost via API?

Pricing is $0.08 per million input tokens and $0.45 per million output tokens, which puts it firmly in the budget tier for a model of this capability. The context window is 131,072 tokens.

How does Gemma 3 27B differ from Gemma 2?

Gemma 3 adds multimodal vision-language input, a much longer context window, and broader multilingual support compared to Gemma 2, which was text-only.

Gemma 3 27B

6168°

#llm #google #vision #multilingual #local #reasoning

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Google | 131,072 token context | $0.08/M input, $0.45/M output. Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

Guides for this model

Ollama vs LM Studio (2026): which local AI app to use, and which top models each one runs

How much VRAM do I actually need for local AI?

Open weights vs open source vs API-only: what you are actually allowed to do

What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)

All guides

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Gemma 3 27B is Google's open-weights multimodal model, capable of reading images as well as text across more than 140 languages. At 27 billion parameters it sits in a practical size range for local use, while the API price is low enough to run at scale. It suits developers and researchers who want a capable, deployable model without a closed-source lock-in.

Known for

Vision-language input: attach an image and ask questions about it.
128k token context window for long documents and conversations.
Broad multilingual coverage across 140-plus languages.
Structured outputs and function calling for agentic pipelines.
Open weights: download and run it yourself.

Watch out for

27B parameters demands serious VRAM locally; plan for 24 GB or more.
Weights are open but check Google's Gemma licence before any commercial deployment.
Vision capability is input-only; it reads images but produces text, not images.

Best for

Prompts to try

Analyse an attached image

I'm attaching a screenshot of a data table. List every column header you can see, describe what the table appears to measure, and flag any cells that look like outliers or errors. Be specific about row and column positions.

Multilingual document summary

The following text is a product liability clause from a Spanish-language contract. Summarise the key obligations and limitations it places on the supplier, then identify any terms that would be unusual under English common law. Text: [paste clause here]

Function-calling plan

I have three tools: search_web(query), read_file(path), and send_email(to, subject, body). A user asks: 'Find the latest EU AI Act summary online, save the key points to notes.txt, then email them to legal@example.com.' Write the ordered sequence of tool calls with arguments to complete this task.

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Questions people ask

Can I run Gemma 3 27B locally?: Yes, the weights are open and available on Hugging Face at google/gemma-3-27b-it. At 27 billion parameters you will want at least 24 GB of VRAM for comfortable inference; quantised GGUF versions reduce that requirement.
What does Gemma 3 27B cost via API?: Pricing is $0.08 per million input tokens and $0.45 per million output tokens, which puts it firmly in the budget tier for a model of this capability. The context window is 131,072 tokens.
How does Gemma 3 27B differ from Gemma 2?: Gemma 3 adds multimodal vision-language input, a much longer context window, and broader multilingual support compared to Gemma 2, which was text-only.

Captain's verdict
A rare combination: open weights, vision input, long context and a price that does not punish you for using it.

Model card on Hugging Face

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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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

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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.
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.

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