AI Glossarymodels

Llama

Meta's open-source large language model family that can be downloaded, modified, and self-hosted without API fees.

How It Works

Llama (Large Language Model Meta AI) is Meta's open-weight model series. Unlike GPT and Claude, Llama models can be downloaded and run on your own hardware. This means no per-token API costs, full data privacy (nothing leaves your servers), and the ability to fine-tune without restrictions. The tradeoff is that you need GPU infrastructure to run them. Llama models are available in various sizes (7B, 13B, 70B+ parameters). Smaller models can run on consumer GPUs or even laptops using quantization techniques. Larger models rival commercial APIs in quality but require serious hardware. Frameworks like Ollama, vLLM, and llama.cpp make self-hosting accessible. For production use, Llama makes sense when: (1) you need data sovereignty (healthcare, finance, government), (2) your volume is high enough that API costs exceed hosting costs, or (3) you need a heavily customized model. Most startups should start with commercial APIs and consider Llama when they hit scale.

Common Use Cases

  • 1Self-hosted AI for data privacy
  • 2High-volume inference without API costs
  • 3Custom fine-tuning without restrictions
  • 4On-device AI applications
  • 5Research and experimentation

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

Inference

The process of running a trained AI model to generate predictions or outputs from new inputs, as opposed to training the model.

Open-Source AI

AI models whose weights and architecture are publicly available, allowing anyone to inspect, modify, run, and build upon them.

Quantization

A technique that reduces AI model size and memory requirements by using lower-precision numbers to represent model weights, trading a small accuracy loss for major efficiency gains.

Need help implementing Llama?

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