Llama 4 Scout 17B 16E
Llama 4 Scout 17B 16E is a chat model developed by Meta AI, leveraging a mixture-of-experts architecture to enable text and multimodal experiences, including text and image understanding. It is genuinely best at handling a wide range of inputs, including text and images, and supporting multiple languages, with capabilities such as function calling, JSON mode, and text generation.
Input
Output
Context
262K
Max Output
8K
Parameters
108.6B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for Llama 4 Scout 17B 16E
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 4 Scout 17B 16E
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama 4 Scout 17B 16E advertises a context window of 262,144 tokens, with a maximum output of 8,192 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 4 Scout 17B 16E is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Llama 4 Scout 17B 16E 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 4 Scout 17B 16E 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 4 Scout 17B 16E 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 105.5 GB at INT8 for the weights and a default context, from the catalog's 108.6B 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 13, 2026
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