Back
All Meta models
LLMS • CURATED • UPDATED FEB 5, 2026

Llama

Meta's open-source large language model

Llama is Meta AI's open-source large language model family with multiple versions: Llama (February 2023), Llama 2 (July 2023), Llama 3 (April 2024), Llama 3.1 405B (405B parameters, July 2024), Llama 3.3 (December 2024), Llama 4 Maverick (April 2026), and Llama 4 Scout (April 2026). Designed for research and commercial use with strong performance across text generation, reasoning, and code tasks. Available in various sizes from 7B to 405B parameters. Supports multiple languages and extended context windows. Available through Meta's official channels, Hugging Face, and various cloud providers. Open-source licensing allows for local deployment and customization.

Pricing Free
Platforms api, local
API Yes
Open Source Yes
Modalities LLMs
Best For Best for Open Source
Date Added 2026-02-05

Meta's flagship open-source LLM with strong performance, extensive model sizes, and permissive licensing for research and commercial use.

Visit llama.meta.com to access model downloads and documentation. Request access to model weights through Meta's official channels. For API access, use cloud providers like Together AI, Replicate, or Hugging Face Inference API. For local deployment, download model weights and use compatible frameworks like llama.cpp, vLLM, or Transformers. Follow Meta's documentation for setup and fine-tuning instructions.

1 Choose appropriate model size based on your hardware and use case
2 Leverage open-source community resources and fine-tuning guides
3 Use quantized versions for efficient local deployment
4 Take advantage of extended context windows in Llama 3.1 and Llama 4
5 Explore fine-tuning capabilities for domain-specific applications
Website Documentation GitHub Hugging Face
Claude Fable 5 NotebookLM Claude Opus 5 GPT-5.6 Sol Kimi K3

Local AI Deployment

Deploy Llama models locally for privacy-sensitive applications or offline use cases.

STEPS:
  1. Download appropriate model size from Meta or Hugging Face
  2. Set up inference framework (llama.cpp, vLLM, or Transformers)
  3. Configure model parameters and context windows
  4. Integrate into your application

Custom Model Fine-tuning

Fine-tune Llama models for specific domains or tasks using your own data.

STEPS:
  1. Prepare your training dataset
  2. Set up fine-tuning environment (PyTorch, Hugging Face)
  3. Configure training parameters and hyperparameters
  4. Train and evaluate your fine-tuned model
Free Completely free
📚

Open Models: What They Cannot Do

The benchmark gap has largely closed: open models now score within a point of the closed frontier on...

Hardware For Local AI Models: What The Memory Shortage Changed

Capacity decides whether a model runs. Bandwidth decides how fast it talks, and it is the number mos...

How To Run AI Models Locally: Hardware, Quantisation And Engines

Running a frontier open model locally is a memory problem before it is a speed problem, and a bandwi...

Uncensored AI Models: What Abliteration Actually Does To Them

Abliteration removes a model's ability to refuse by deleting one direction from its activations. It ...

Which LLMs Actually Deliver in 2026?

Comprehensive comparison of the best large language models in 2026 including ChatGPT, Claude, Gemini...

View Llama Alternatives (2026) →

Compare Llama with 5+ similar llms AI tools.

Q

Is Llama free?

A

Yes, Llama is completely free to use.

Q

Does Llama have an API?

A

Yes, Llama offers an API for programmatic integration.

Q

What is Llama best for?

A

Llama is best for Open Source. Llama is Meta AI's open-source large language model family with multiple versions: Llama (February 2023), Llama 2 (July 2023), Llama 3 (April 2024), Llama 3. Meta's flagship open-source LLM with strong performance, extensive model sizes, and permissive licensing for research and commercial use.

Q

What platforms does Llama support?

A

Llama supports api, local.

Q

Is Llama open source?

A

Yes, Llama is open source. You can access the source code on GitHub at https://github.com/meta-llama.

Q

How do I get started with Llama?

A

Visit llama.meta.com to access model downloads and documentation. Request access to model weights through Meta's official channels. For API access, use cloud providers like Together AI, Replicate, or Hugging Face Inference API. For local deployment, download model weights and use compatible framewor...

Q

How do I use Llama?

A

Llama is a large language model for text generation, analysis, and conversation. Use the API for programmatic access. Enter prompts or questions to get responses. It excels at fully open-source with permissive licensing.

🏷️

Work on Llama? You're hand-reviewed in our directory. Add this badge to your site — it links back to this profile.

Featured on CuratedAI