Llama 4 Scout

Llama 4 Scout is useful to evaluate because it combines a specific architecture, license and operating envelope rather than simply adding another row to a model leaderboard.

Meta reports roughly 40T pretraining tokens for Scout, an August 2024 knowledge cutoff, broad multilingual pretraining and a multimodal training pipeline. Post-training follows the Llama 4 family approach combining supervised fine-tuning, reinforcement learning and preference optimization.

From an infrastructure perspective, the key sizing facts are ~109B total parameters, 17B/token active parameters and 10,000,000 tokens of context. These values should be read together: weight memory, active compute and KV-cache growth describe different resource constraints.

From a governance perspective, the checkpoint is distributed under Llama 4 Community License Agreement. OpenWeightModels records that independently from the fact that the weights are downloadable, because “open weight” is an access classification—not a universal statement about commercial, redistribution or derivative-work rights.

Llama 4 Scout uses sparse expert routing rather than activating its complete parameter pool for every token. The distinction between ~109B total parameters and 17B/token active parameters is central to capacity planning: active parameters influence compute per token, while the complete expert set still affects storage, model loading, sharding and network topology.

This is why OpenWeightModels never maps the active-parameter figure directly to a dense-model hardware class. A sparse model can be computationally efficient per token and still require datacenter-class memory and interconnect to keep all experts available.

Publisher-documented architecture details for this checkpoint include the elements below. These details are more useful for deployment planning than a parameter count alone because attention layout, expert routing, modality encoders and context design can affect throughput and memory independently.

Scout is substantially smaller than Maverick in total parameter count but still stores roughly 109B parameters. The 10M-token context can dominate memory through KV cache; very-long-context deployment should be planned as a specialized workload rather than a default setting.

Real memory use includes model weights, KV cache, activations/buffers, multimodal encoders when applicable, framework overhead and batching. A published “fits on” statement is meaningful only together with precision, context, batch and host/GPU topology.

Llama 4 Scout is most relevant when the application specifically values very-long-context multimodal understanding, assistants, documents and code. That should be balanced against its hardware class, context behavior, license and modality requirements.

A smaller model can be operationally superior when latency, privacy, device deployment or predictable cost matter more than peak benchmark capability. Conversely, a larger sparse model may justify its complexity when the workload benefits from higher capacity, sophisticated reasoning or agent behavior.

The selection decision should therefore compare at least five dimensions: required task quality, data/control requirements, legal eligibility, serving cost and ecosystem/runtime support. OpenWeightModels exposes these dimensions separately so a model is not selected on benchmark reputation alone.

Last full review: 29 September 2026. Publisher model cards, repositories and license texts are preferred. Runtime claims are attached to the relevant runtime/provider rather than inferred from the base checkpoint.

Where documentation conflicts or a field is ambiguous, the passport uses the more conservative interpretation and explains the discrepancy instead of silently selecting the largest number.