Meta Llama 3.3
Last Updated on: Sep 12, 2025
0
0Reviews
5Views
0Visits
What is Meta Llama 3.3?
Llama 3.3 is Meta’s instruction-tuned, text-only large language model released on December 6, 2024, available in a 70B-parameter size. It matches the performance of much larger models using significantly fewer parameters, is multilingual across eight key languages, and supports a massive 128,000-token context window—ideal for handling long-form documents, codebases, and detailed reasoning tasks.
Who can use Meta Llama 3.3 & how?
- 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.
What's so unique or special about Meta Llama 3.3?
- 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.
Things We Like
- 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
Things We Don't Like
- 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
Photos & Videos
Pricing
FreeThis AI is free to use
ATB Embeds
Reviews
Proud of the love you're getting? Show off your AI Toolbook reviews—then invite more fans to share the love and build your credibility.
Product Promotion
Add an AI Toolbook badge to your site—an easy way to drive followers, showcase updates, and collect reviews. It's like a mini 24/7 billboard for your AI.
Reviews
0 out of 5
Rating Distribution
5 star
0
4 star
0
3 star
0
2 star
0
1 star
0
Average score
Ease of use
0.0
Value for money
0.0
Functionality
0.0
Performance
0.0
Innovation
0.0
Popular Mention
FAQs
Meta’s December 6, 2024 instruction-tuned LLM with 70B parameters and a 128K-token context window, optimized for multilingual and instruction-based tasks.
Up to 128,000 tokens, ideal for processing long documents or code in one go.
Eight languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Delivers similar or superior performance to Llama 3.1 405B while being more efficient, thanks to better fine-tuning and architecture.
Yes—released under a community license, with support on major cloud platforms including OCI since February 2025.
Similar AI Tools
No similar AIs available.
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.
If you have any suggestions or questions, email us at hello@aitoolbook.ai