- Mobile & Edge Developers: 1B and 3B variants run locally on smartphones or IoT devices—even ARM-based chips.
- AI Developers & Enterprises: 11B/90B Vision models support image tasks like charts, OCR, and document analysis.
- Researchers & Tool Builders: Use text-only or vision models with long-context input for summarization, retrieval, or reasoning.
- Educators & Multilingual Applications: Multilingual text and vision assist in education, translation, and interactive learning.
- Open-Source Advocates: All sizes available under the Llama 3.2 community license, ensuring open deployment and fine-tuning opportunities.
How to Use Llama 3.2?
- Select Model: Choose 1B/3B for text-only tasks on-device; 11B/90B Vision via API/cloud.
- Deploy on Edge or Cloud: Use Hugging Face, AWS Bedrock, IBM watsonx.ai, or mobile runtimes.
- Send Prompts: Provide up to 128K tokens per prompt—texts or mixed media for Vision models.
- Perform Tasks: Summarization, visual Q&A, chart interpretation, code generation, multilingual chat, etc.
- Optimize for Efficiency: Edge variants designed for privacy and speed, while Vision models use GQA and efficient GPU inference.
- Edge-Ready Text Models: 1B/3B run locally with low latency and minimal resource use.
- First Open Multimodal: 11B/90B Vision models rival closed-source systems in chart and diagram understanding.
- Huge Context Window: 128K-token support enables long document and code workflows.
- Efficient Inference: GQA boosts speed, making even Vision models practical at scale.
- Open-Source & Commercial Flexibility: Community license supports widespread adoption and customization.
- Open-source 70B model matching much larger LLMs
- Massive 128K context for long documents and codebases
- Strong multilingual and instruction-following capabilities
- Available on major platforms including Oracle OCI
- Efficient inference via GQA and transformer optimizations
- Vision and multimodal features are not supported—text-only model
- Larger hardware requirements than smaller open models
- Context window still falls short of million-token ultra-long needs
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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