Llama Guard 3 11B Vision
Meta AI's Llama Guard 3 11B Vision is a chat model capable of image understanding, reasoning, and text generation, optimized for content safety classification and detecting harmful multimodal prompts and responses. It is genuinely best at supporting image reasoning use cases and safeguarding content for both text and image inputs.
Input
Output
Context
128K
Max Output
2K
Parameters
10.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Llama Guard 3 11B Vision
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 Llama Guard 3 11B Vision
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama Guard 3 11B Vision advertises a context window of 128,000 tokens, with a maximum output of 2,048 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Llama Guard 3 11B Vision is an open-weight model released under the Llama3.2 license, so the weights can be downloaded and self-hosted within that license's terms.
Llama Guard 3 11B Vision 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.
No published benchmark result for Llama Guard 3 11B Vision is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Llama Guard 3 11B Vision is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 10.8 GB at INT8 for the weights and a default context, from the catalog's 10.7B 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, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 15, 2026
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