GoogleGemma 3 12B
Gemma 3 12B It, developed by Google, is a multimodal chat model capable of handling text and image inputs and generating text outputs, with a context window of 32,768 tokens. It is well-suited for tasks such as question answering, summarization, and reasoning, and its open-source license allows for flexible deployment.
Input
Output
Context
131K
Max Output
8K
Parameters
12.2B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Benchmarks
Estimated GPU requirements for Gemma 3 12B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Gemma 3 12B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Gemma 3 12B advertises a context window of 131,072 tokens, with a maximum output of 8,192 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Gemma 3 12B is an open-weight model released under the Gemma license, so the weights can be downloaded and self-hosted within that license's terms.
Gemma 3 12B accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
Gemma 3 12B has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Gemma 3 12B is shown as not available rather than estimated.
Yes. Gemma 3 12B is served on the managed pool through the OpenAI-compatible endpoint as google-gemma-3-12b-it, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 12.3 GB at INT8 for the weights and a default context, from the catalog's 12.2B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 16, 2026
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