Llama 4 Maverick 17B 128E Instruct
Llama 4 Maverick 17B 128E Instruct FP 8 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 a notable technical trait being its large context window of 1,000,000 tokens.
Input
Output
Context
1049K
Max Output
-
Parameters
401.6B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Served variants
The same model in other encodings or serving configurations. Requesting meta-llama-4-maverick-17b-128e-instruct lets routing pick among them; the ids below pin one build.
meta-llama-4-maverick-17b-128e-instruct:fp8quantized
Benchmarks
Resources & Links
Estimated GPU requirements for Llama 4 Maverick 17B 128E Instruct
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 Maverick 17B 128E Instruct
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama 4 Maverick 17B 128E Instruct advertises a context window of 1,048,576 tokens. 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 Maverick 17B 128E Instruct 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 Maverick 17B 128E Instruct 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.
Llama 4 Maverick 17B 128E Instruct 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 Llama 4 Maverick 17B 128E Instruct is shown as not available rather than estimated.
Yes. Llama 4 Maverick 17B 128E Instruct is served on the managed pool through the OpenAI-compatible endpoint as meta-llama-4-maverick-17b-128e-instruct, 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 387.2 GB at INT8 for the weights and a default context, from the catalog's 401.6B 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 22, 2026
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