Llama 4 Scout

Llama 4 Scout launched on April 5, 2025 with a large 10 million-token input window. Its blend of 17 billion active parameters (16 experts, 109 billion total parameters) and mixture-of-experts (MoE) architecture keeps it highly efficient, fitting on a single NVIDIA H100 GPU with quantization, while delivering strong performance across reasoning, coding, and multimodal benchmarks. Llama 4 Scout is offered under Meta’s open-weight license for research, enterprise, and developer use.

What it’s optimized for

Llama 4 Scout is purpose-built for:

• Extreme long-context processing up to 10 million tokens for multi-document, codebase, or activity stream workflows

• Cost-efficient deployment on a single GPU, even with massive context

• Visual question answering, chart and table reasoning, and document parsing at scale

• Real-time summarization, analysis, and parsing on extensive, unchunked datasets

Typical use cases

Llama 4 Scout excels at:

• Multi-document or book-scale summarization and translation

• Reasoning over vast session histories or entire legal/code corpora in one pass

• Complex visual question answering (VQA), chart/graph explanations, and long-form document Q&A

• Activity parsing, event extraction, and analytics from logs or conversation transcripts

• Efficient multimodal applications requiring both vision and text inputs

Key characteristics

• 17 billion active parameters in 16 experts; 109 billion parameters total

• Fits on a single NVIDIA H100 GPU with quantization (Int4/BF16)

• Open weight release for broad research and enterprise use, subject to license with limits for extremely high-usage deployment

• Trained from scratch, without “codistillation” from larger models but with extensive multimodal data

Model architecture

Llama 4 Scout utilizes Meta’s mixture-of-experts transformer architecture, activating only a subset of total parameters per token for efficiency and scalability.

Designed from scratch, it underwent both pre-training and post-training with focus on length generalization, leveraging early fusion for natively multimodal learning (text, image, video). Quantization optimizations and specialized attention kernels enable its single-GPU footprint even at massive context windows. The model supports rapid inference and task flexibility, delivering SOTA performance on multimodal reasoning without excessive hardware overhead.

Why choose 1RPC.ai for Llama 4 Scout

• Every call is directly tied to the exact model and version used, ensuring traceability and trust in your outputs

• Execution runs inside hardware-backed enclaves, so the relay can’t access or log your request

• Connect to multiple AI providers through a single API

• Avoid provider lock-in with simple, pay-per-prompt pricing

• Privacy by design with our zero-tracking infrastructure that eliminates metadata leakage and protects your activity

Summary

Llama 4 Scout represents a major leap for open, accessible AI: Offering a large context length, powerful multimodal intelligence, and favorable benchmark results in a compute-efficient package. Its architecture and training make it ideal for document analysis, codebase reasoning, visual tasks, and large-scale enterprise applications, all without the resource burden of typical foundation models.

Scout is the go-to model when you need vast context capacity, top-tier visual and text reasoning, and efficient deployment, all open and ready for next-generation AI development.