Llama 4 Local Setup: Run Meta's Multimodal AI on Your PC

What's New in Llama 4

Meta's Llama 4, released in early 2026, represents a major leap forward:

Key Features

Model Variants

Llama 4 Scout - The efficient option

• 109B total parameters, 17B active
• Ideal for most local deployments
• 12GB VRAM (Q4 quantization)
• 45 tokens/sec on RTX 4090

Llama 4 Maverick - The balanced choice

• 400B total parameters, 17B active
• Best quality-to-resource ratio
• 24GB VRAM (Q4 quantization)
• 38 tokens/sec on RTX 4090

Llama 4 Behemoth - The research giant

• 2T total parameters, 288B active
• State-of-the-art performance
• 128GB+ VRAM required
• Enterprise/research deployments

Local Setup Guide

Step 1: Install Ollama

# macOS/Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows - download from ollama.com

Step 2: Pull Llama 4

# Scout (12GB VRAM)
ollama pull llama4-scout

# Maverick (24GB VRAM) - Recommended
ollama pull llama4-maverick

Step 3: Run and Test

ollama run llama4-maverick

Test multimodal:

>>> Describe this image: /path/to/image.jpg

Step 4: Configure for Performance

Create a custom Modelfile:

cat > Llama4Modelfile << 'EOF'
FROM llama4-maverick

# Optimal settings for local use
PARAMETER num_ctx 8192
PARAMETER temperature 0.7
PARAMETER top_p 0.9

SYSTEM "You are Llama 4, a helpful AI assistant with vision capabilities."
EOF

ollama create llama4-custom -f Llama4Modelfile

Hardware Requirements

VRAM Requirements

Recommended Configurations

Apple Silicon

Using Vision Capabilities

Image Analysis

import ollama

response = ollama.chat(
    model='llama4-maverick',
    messages=[{
        'role': 'user',
        'content': 'What do you see in this image?',
        'images': ['./chart.png']
    }]
)
print(response['message']['content'])

Document Processing

# Analyze a PDF page as image
from pdf2image import convert_from_path
import ollama

pages = convert_from_path('document.pdf')
pages[0].save('page1.png', 'PNG')

response = ollama.chat(
    model='llama4-maverick',
    messages=[{
        'role': 'user',
        'content': 'Extract and summarize the key information from this document.',
        'images': ['page1.png']
    }]
)

Code Understanding from Screenshots

response = ollama.chat(
    model='llama4-maverick',
    messages=[{
        'role': 'user',
        'content': 'Explain what this code does and identify any bugs.',
        'images': ['code_screenshot.png']
    }]
)

Benchmark Results

Language Understanding

Coding

Vision

Integration Examples

LangChain Integration

from langchain_ollama import ChatOllama

llm = ChatOllama(
    model="llama4-maverick",
    temperature=0.7
)

response = llm.invoke("Explain quantum computing simply")
print(response.content)

Open WebUI Setup

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  ghcr.io/open-webui/open-webui:main

Access http://localhost:3000 and select llama4-maverick.

Llama 4 vs Competition

Key Takeaways

1. Llama 4 brings multimodal capabilities to open-source AI
2. MoE architecture enables better quality without more VRAM
3. Maverick is the sweet spot for most local users
4. 24GB VRAM (RTX 4090) runs Maverick well
5. Vision capabilities rival GPT-4V and Gemini

Next Steps

1. Compare to DeepSeek R1 for reasoning tasks
2. Build multimodal agents with Llama 4
3. Set up RAG with vision-enhanced retrieval
4. Optimize your GPU for Llama 4

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Llama 4 makes enterprise-grade multimodal AI accessible to everyone. With Scout and Maverick running on consumer hardware, the gap between local and cloud AI continues to shrink.