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Model Library
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Llama 4 Scout 17B 16ELlama 4 Maverick 17B 128E InstructLlama 4 Maverick 17B 128ELlama 4 Scout 17B 16E Instruct
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  4. Meta Llama 4
  5. Llama 4 Maverick 17B 128E

Llama 4 Maverick 17B 128E

Llama 4 Maverick 17B 128E is a natively multimodal mixture-of-experts (MoE) large language model engineered for high-performance text and image understanding. It activates 17B parameters (400B total) across 128 experts and is built on a 48-layer transformer architecture with 40 attention heads and a 5,120 hidden size. The model supports multilingual text and image inputs and produces multilingual text and code outputs. With up to a 1M token context window, it is optimized for scalable multimodal AI systems, large-document reasoning, and research-grade long-context workflows.

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About this model

BlogHF Model Card

Llama 4 Maverick 17B 128E is a natively multimodal mixture-of-experts (MoE) large language model engineered for high-performance text and image understanding. It activates 17B parameters (400B total) across 128 experts and is built on a 48-layer transformer architecture with 40 attention heads and a 5,120 hidden size. The model supports multilingual text and image inputs and produces multilingual text and code outputs. With up to a 1M token context window, it is optimized for scalable multimodal AI systems, large-document reasoning, and research-grade long-context workflows.

Model Type

Vision-Language Model

Released

April 5, 2025

License: Llama4

Parameters

400B

Model ID
meta-llama/Llama-4-Maverick-17B-128E
Capabilities
Text GenerationInstruction FollowingReasoningMathematical ReasoningCode Generation+5

Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meta-llama/Llama-4-Maverick-17B-128E). Released under Llama4. Further reading: Blog

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Hardware

MI325X

2,048 GB

B200

1,440 GB

Engine

vLLM
SGLang

Deployment code

INFERENCE COMMAND
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Performance under load

23k18k14k9.2k4.6k0163264128256512Total Tokens / secConcurrent requests
Tokens/s (total)

Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.

SS

Shivam Sharma

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AMD Instinct MI325X

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AMD Instinct MI325X

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