Llama 4 Scout: 10M Context, Native Multimodal

Overview

Llama 4 Scout represents Meta's shift to Mixture of Experts architecture. With 109B total parameters but only 17B active per token across 16 experts, Scout achieves Llama 3.3 70B-level quality while being dramatically more efficient at inference time.

The headline features: a 10 million token context window (the largest of any open model) and native multimodal support for text and images using early fusion — no separate vision encoder needed.

Trained on 40 trillion tokens across 200 languages, Scout is one of the most broadly capable open models available. On consumer hardware, it runs on an RTX 4090 (24GB) with aggressive quantization.

Architecture Deep Dive

Why MoE Matters for Local AI

Scout activates only 17B of its 109B parameters per token — 15.6% utilization. This means inference speed is comparable to a 17B dense model, but with the quality of a model that has seen all 109B parameters during training. The tradeoff: you still need enough memory to hold all 109B parameters.

iRoPE: How 10M Context Works

Traditional RoPE (Rotary Position Embedding) degrades at long sequences. Scout uses interleaved RoPE (iRoPE), which alternates between standard and modified attention patterns to maintain quality across millions of tokens. In practice, running 10M context requires server-grade hardware — most local users will run at 8K-128K context where iRoPE still provides better long-range understanding than standard RoPE models.

Benchmarks

Quick Start with Ollama

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"
)

# Text-only query
response = client.chat.completions.create(
    model="llama4:scout",
    messages=[
        {"role": "user", "content": "Explain MoE architectures in 3 sentences"}
    ]
)

# Multimodal query (image + text)
import base64
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="llama4:scout",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What does this chart show?"},
            {"type": "image_url", "image_url": {
                "url": f"data:image/png;base64,{image_data}"
            }}
        ]
    }]
)

# Pull Scout and start Open WebUI
ollama pull llama4:scout
docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  ghcr.io/open-webui/open-webui:main

Hardware Requirements

Model Comparisons

Multimodal Capabilities

Scout uses early fusion — text and vision tokens are unified within the foundational architecture, not bolted on via a separate encoder. This means:

For vision tasks running locally, Scout is the strongest open option — no need for separate models like LLaVA or PaLIGemma. See our multimodal AI guide for more on local image understanding.

Best Use Cases

Advanced Setup

# Llama 4 Scout Modelfile
FROM llama4:scout

# Optimize for document analysis
PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER num_ctx 65536

SYSTEM """You are a precise document analyst. When given images
or text, extract key information accurately. Cite specific
sections. Be thorough but concise."""

ollama create scout-analyst -f Modelfile
ollama run scout-analyst

# Download from Unsloth (HuggingFace)
# Model: unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit
# Then convert to GGUF for Ollama

# Or use llama.cpp directly with 1.78-bit GGUF
./llama-cli -m Llama-4-Scout-1.78bit.gguf \
  -ngl 99 -c 8192 \
  --temp 0.3 -p "Explain quantum computing"

# With 32K context (fits in 24GB + overhead)
ollama run llama4:scout --num-ctx 32768

# For longer documents, increase if VRAM allows
ollama run llama4:scout --num-ctx 131072

Sources

• Meta: Llama 4 Models — Official specifications and benchmarks
• Hugging Face: meta-llama/Llama-4-Scout-17B-16E — Model card and weights
• Hugging Face Blog: Welcome Llama 4 — Release announcement and analysis
• Ollama: llama4 — Ollama model library