Llama 4 Maverick 400B

Overview

Meta's larger Llama 4 MoE at 400B total with 17B active across 128 experts, natively multimodal. LMArena 1417 and 1M token context, but 245GB to download.

When to pick this model

• Frontier-quality open chat in multi-GPU production
• Multimodal agents needing 1M context
• Drop-in for teams ready to commit to the Llama 4 ecosystem
• Workloads where MMLU-Pro 80 quality justifies the storage cost

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

• LMArena 1417 — top-tier open chat quality
• MMLU-Pro 80
• 1M token context
• Native multimodal with strong vision performance
• 17B active keeps inference cost manageable

Limitations

• 245GB download — non-trivial storage and bandwidth
• Hugging Face gated access
• Llama 4 Community License with >700M MAU clause
• Outclassed on reasoning by R1-class models

Architecture & training

Architecture: MoE 128 experts · 400B/17B active · natively multimodal · 1M ctx

Training: Scout's big brother.

Quick start

ollama run llama4:maverick

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