FreeLLMMeta releases Llama 4 Scout and Maverick, 17B‑parameter MoE models with 10M context
Llama 4 Scout fits a single H100 GPU, while Maverick runs on a single H100 host, both using MoE routing for multimodal AI.
Meta announced two new mixture‑of‑experts (MoE) models: Llama 4 Scout and Llama 4 Maverick.
Both models have 17 billion active parameters. Scout uses 16 experts and fits on a single NVIDIA H100 GPU with Int4 quantisation.
Maverick employs 128 experts, fits on a single H100 host, and activates 17 B of its 400 B total parameters per token.
"best multimodal model in its class" is used to describe both Scout and Maverick in Meta’s release.
O que isso significa para você
For developers, the models provide high‑quality multimodal performance on a single H100, lowering hardware costs for inference. Users of free or locally hosted models still rely on smaller, non‑MoE solutions, but the open‑weight release may inspire community‑driven equivalents.
Fontes
- MoE Routing Strategies Explained: Top-K, Expert-Choice, Sigmoid Gating and Dynamic Routing (2026 Guide), MarkTechPost, 8 Nov 2026
- The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation, ai.meta.com (official), 5 Apr 2025
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