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Introducing Vertex AI's Llama-4-Scout-128B-16E-Instruct-MAAS: Powerful Multimodal AI at Cost-Effective Pricing

Tal Peretz

— 2 min read
Introducing Vertex AI's Llama-4-Scout-128B-16E-Instruct-MAAS: Powerful Multimodal AI at Cost-Effective Pricing

Google Cloud's Vertex AI has introduced an exciting new managed AI endpoint: the Llama-4-Scout-128B-16E-Instruct-MAAS. Leveraging Meta’s latest advancements in multimodal AI, this model brings powerful performance, efficient inference, and robust multimodal capabilities directly to your applications, all at competitive pricing.

Exploring the Vertex AI Llama-4-Scout-128B-16E-Instruct-MAAS

The Llama-4-Scout-128B-16E-Instruct-MAAS is built on a sophisticated Mixture-of-Experts (MoE) architecture featuring 16 active experts per token. With 128 billion total parameters—of which approximately 17 billion are activated per token—this model delivers excellent performance while maintaining efficient inference on a single NVIDIA H100 GPU. MoE enables the model to activate only the necessary parameters per inference, significantly reducing computational overhead without sacrificing quality.

Key Features at a Glance

  • Multimodal Capabilities: Seamlessly processes both text and images, ideal for visual question-answering, document summarization, and code analysis.
  • Efficient Architecture: Runs efficiently on a single GPU with int4 quantization, enabling lower latency and reduced operational costs.
  • Managed Deployment: Fully managed by Vertex AI, eliminating the need to handle hardware provisioning, scaling complexities, and operational maintenance.
  • Competitive Pricing: Input tokens are priced at $0.25 per million tokens and output tokens at $0.70 per million tokens, delivering excellent cost efficiency for multimodal tasks.
  • Function Calling Support: Integrates easily into applications with built-in function calling capabilities.
  • Flexible Context Handling: Supports up to 10 million tokens per request, making it suitable for extensive context management and complex reasoning tasks.

Practical Use Cases & Benefits

The Llama-4-Scout-128B-16E-Instruct-MAAS is particularly valuable for:

  • Visual Document Analysis: Quickly and accurately summarize documents with visual elements, such as charts and graphs.
  • AI-Driven Assistants: Enhance AI assistant capabilities with multimodal understanding for richer, more intuitive interactions.
  • Long-Context Reasoning: Efficiently handle comprehensive summarization, cross-referencing, and retrieval-augmented generation tasks.

Quickstart: Getting Started on Vertex AI

Deploying and interacting with the Llama-4-Scout model is easy via the Vertex AI SDK. Here's a quick example to get you started:


from google.cloud import aiplatform

aiplatform.init(project='YOUR_PROJECT', location='YOUR_REGION')

endpoint = aiplatform.Endpoint('YOUR_LLAMASCOUT_ENDPOINT_ID')
response = endpoint.predict(instances=[
    {'text': 'Summarize the following report:', 'images': [{'imageBytes': '...base64data...'}]}
])

print(response)

Ensure you replace 'YOUR_PROJECT', 'YOUR_REGION', and 'YOUR_LLAMASCOUT_ENDPOINT_ID' with your specific configurations.

When to Choose Vertex AI Llama-4-Scout

  • When your application demands both textual and visual analysis.
  • If rapid scaling, low latency, and managed infrastructure are priorities.
  • When cost-effective multimodal AI inference is essential.

Considerations & Limitations

  • No batch prediction support; optimized for interactive, real-time inference.
  • Not suitable for on-premises or privately hosted environments.
  • Content moderation capabilities (e.g., Llama Guard) require separate integration.

Conclusion

Vertex AI’s Llama-4-Scout-128B-16E-Instruct-MAAS offers enterprises and developers a powerful, multimodal AI model, delivering exceptional capabilities, ease of deployment, and cost efficiency. This new managed AI service significantly simplifies the integration of sophisticated AI into your applications, empowering your projects with advanced multimodal intelligence.

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