Llama 4 Scout 109B

Overview

Meta's compact Llama 4 MoE — 109B total, 17B active, natively multimodal, with an unprecedented 10M token context. Fits on a single H100.

When to pick this model

• Whole-codebase or whole-corpus analysis up to 10M tokens
• Multimodal pipelines where one H100 is the inference budget
• Long-form document understanding without RAG
• Multilingual chat with native image input

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• 10M token context — unmatched among open models
• Runs on a single H100 thanks to MoE sparsity
• Native multimodal input — no separate vision adapter needed
• 17B active parameters keeps inference fast

Limitations

• Hugging Face gated access
• Llama 4 Community License with the >700M MAU clause
• Long-context quality drops well before the 10M ceiling
• Newer than Llama 3.1 — tooling still catching up

Architecture & training

Architecture: MoE 16 experts · 109B/17B active · iRoPE · natively multimodal

Training: Meta Llama 4 compact flagship.

Quick start

ollama run llama4:scout

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.