Model ID
meta-llama/Llama-4-Scout-17B-16E
Capabilities
Llama 4 Scout 17B 16E
Llama 4 Scout 17B 16E is a natively multimodal mixture-of-experts (MoE) large language model built for advanced text and image understanding. It activates 17B parameters (109B total) across 16 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 10M token context window, it is optimized for long-context reasoning, large-scale document analysis, and production-ready multimodal AI deployments.
About this model
Llama 4 Scout 17B 16E is a natively multimodal mixture-of-experts (MoE) large language model built for advanced text and image understanding. It activates 17B parameters (109B total) across 16 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 10M token context window, it is optimized for long-context reasoning, large-scale document analysis, and production-ready multimodal AI deployments.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meta-llama/Llama-4-Scout-17B-16E). 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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