Model ID
meta-llama/Llama-4-Maverick-17B-128E-Instruct
Capabilities
Llama 4 Maverick 17B 128E Instruct
Llama 4 Maverick 17B 128E Instruct is an instruction-tuned, natively multimodal mixture-of-experts (MoE) large language model optimized for advanced instruction following and vision-language reasoning. 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 generates multilingual text and code outputs. With a 1M token context window, it is designed for large-scale multimodal workloads, enterprise AI systems, and long-context assistant-style applications.
About this model
Llama 4 Maverick 17B 128E Instruct is an instruction-tuned, natively multimodal mixture-of-experts (MoE) large language model optimized for advanced instruction following and vision-language reasoning. 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 generates multilingual text and code outputs. With a 1M token context window, it is designed for large-scale multimodal workloads, enterprise AI systems, and long-context assistant-style applications.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meta-llama/Llama-4-Maverick-17B-128E-Instruct). Released under Llama4. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
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.
AMD Instinct MI325X
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