Llamaguard 7B
Llamaguard 7B, developed by Meta AI, is a chat model exceling at classifying content in both inputs and responses as safe or unsafe, with the ability to list violating subcategories. It leverages a context window of 4,096 tokens, allowing for comprehensive analysis of text-based inputs.
Input
Output
Context
4K
Max Output
4K
Parameters
6.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Llamaguard 7B
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 Llamaguard 7B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llamaguard 7B advertises a context window of 4,096 tokens, with a maximum output of 4,096 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. Llamaguard 7B is an open-weight model released under the Llama2 license, so the weights can be downloaded and self-hosted within that license's terms.
Llamaguard 7B accepts Text 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 Llamaguard 7B 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; Llamaguard 7B 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 6.9 GB at INT8 for the weights and a default context, from the catalog's 6.7B 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: Aug 28, 2026
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