Deploy Llama 4 with vLLM: Scout vs Maverick Setup Guide (2026)

Meta's Llama 4 is an advanced machine learning model featuring a mixture-of-experts architecture, native multimodal support, and a 10 million token context on its Scout variant, although it requires significant hardware resources for deployment, such as a minimum of 8 H100 GPUs for the Maverick variant. Despite its technological advancements, Llama 4's deployment is restricted in the EU due to licensing issues, likely because of Meta's regulatory challenges with EU authorities. Scout, which can be deployed on a single H100 GPU, is recommended for teams needing long-context capabilities without the extensive hardware demands of Maverick. While Scout provides a unique advantage with its long context capability at a lower hardware cost, Maverick offers superior quality with more experts, suitable for benchmark-critical tasks. Teams must carefully consider hardware requirements, licensing constraints, and whether the model's capabilities align with their needs, especially in light of the EU restrictions, which impede its use by EU-based entities. For those unable to use Llama 4, alternative models like Qwen 3 or DeepSeek-V3 offer similar capabilities without geographic limitations.