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Plain-language AI glossary

Term 20ModelsMeaning / context / connections

Models / Definition

Llama

Meta's open-weight model family, available for download and deployment across cloud, data-center, edge, and local environments under its license.

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MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Llama works.

Llama is Meta's open-weight model family. Meta currently highlights Llama 4 Scout for multimodal work with a large context window and Llama 4 Maverick for image and text understanding. The weights can be obtained from Meta or distribution partners, which gives teams more deployment control than a provider-only API.

Open weights do not mean zero operational cost, unrestricted use, or automatic privacy. Teams still need to read the license, choose hardware or a hosting provider, secure the serving stack, test quality and safety, and account for updates. Local or private deployment can improve data control only when the surrounding system is designed accordingly.

Choose Llama when deployment control, customization, or infrastructure ownership matters enough to justify operating the model stack.

02 / Practical uses

Where it helps.

  1. 01Self-hosted AI for data privacy
  2. 02High-volume inference without API costs
  3. 03Custom fine-tuning without restrictions
  4. 04On-device AI applications
  5. 05Research and experimentation
03 / Keep exploring
Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Open-Source AIAI models whose weights and architecture are publicly available, allowing anyone to inspect, modify, run, and build upon them.QuantizationA 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.InferenceThe process of running a trained AI model to generate predictions or outputs from new inputs, as opposed to training the model.