Unlock GenAI at Scale with Meta Llama 4 + Snowflake Cortex AI
Meta’s latest large language models, Llama 4 Maverick and Scout, are now available directly within Snowflake Cortex AI. This integration is a breakthrough for enterprises seeking secure, multimodal, and scalable AI capabilities — and Data Spot Consulting is ready to help you build with it.
Next-Gen Models for Enterprise AI
Meta’s Llama 4 series introduces advanced reasoning, coding, and multilingual generation across text and image formats. Within Snowflake’s Cortex AI environment, these models offer low-latency inference and strong multimodal performance.
- Llama 4 Scout: 10 million-token context window
- Llama 4 Maverick: Vision-text fused architecture
- Supports 12+ languages and various content types
- Ideal for summarization, agent workflows, and visual Q&A
📘 See more:
Meta Llama 4 Research Overview
Snowflake Cortex AI Docs
Power Your Data Apps Inside Snowflake
Snowflake’s platform now supports direct use of open-source LLMs from Meta alongside others like OpenAI and Anthropic. This makes it a unified foundation for deploying intelligent applications — securely and without leaving your data warehouse.
Your business can now:
- Build copilots and AI agents
- Translate or summarize documents at scale
- Embed smart search in internal tools
- Automate support with multimodal context
How Meta Llama Models Work in Cortex
Meta’s architecture uses Mixture of Experts (MoE) — activating only select parts of the model for each task. This ensures efficiency without compromising intelligence.
Quick specs:
- Scout: 17B active / 109B total parameters
- Maverick: 17B active / 400B total
- Faster token processing with SwiftKV caching
- Supports SQL, Python, and REST API access
Hands-On: Using Llama in Your Workflow
Here’s a SQL example to run content summarization directly from your Snowflake warehouse:
sqlCopyEditSELECT SNOWFLAKE.CORTEX.COMPLETE('llama4-maverick',
[{'role':'user','content':CONCAT('Summarize this input: ',content)}]
,{'guardrails':true})
FROM customer_feedback;
And via REST API:
bashCopyEditcurl -X POST \
-H "Authorization: Bearer <jwt>" \
-H "Content-Type: application/json" \
-d '{
"model": "llama4-maverick",
"messages": [{"role": "user", "content": "Translate this to Spanish: Hello"}],
"max_tokens": 4096
}' \
https://<account>.snowflakecomputing.com/api/v2/cortex/inference:complete
Why Work with Data Spot Consulting
We’re a trusted Snowflake partner that brings experience in LLM deployment, GenAI strategy, and data pipeline engineering. Our team helps you:
- Select the right model per use case
- Fine-tune prompts and guardrails
- Build scalable pipelines and monitoring
- Meet compliance, security, and governance goals
Whether it’s an internal assistant, generative analytics dashboard, or industry-specific AI application — we’ve got your back.
Start Your AI Journey
Connect with us to bring next-gen AI to your data platform. Our experts will help you go from idea to production in weeks — not months.
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