Meta · General LLM

Llama 3

Meta's open-weight large language model family that delivers near-frontier performance while being freely available for research and commercial use.

Overview

Llama 3 is Meta's most capable open-weight language model, available in 8B and 70B parameter variants with a 405B version also released. It achieves performance competitive with leading closed-source models on many benchmarks while being freely available for download and deployment. Llama 3 has become the foundation for thousands of community fine-tunes and the default choice for organizations wanting to self-host powerful language models with full control over their infrastructure.

Parameters

8B / 70B / 405B variants

Context Window

128K tokens

Architecture

Dense decoder-only transformer

Training Data

15T+ tokens

License

Llama 3 Community License (permissive)

Capabilities

General-purpose text generation and reasoning

Code generation across multiple programming languages

Instruction following and conversational AI

Multilingual text understanding and generation

Fine-tuning and customization for specific domains

Use Cases

Self-hosting production AI systems with full data privacy control

Fine-tuning domain-specific models for enterprise applications

Building on-premise AI solutions for regulated industries

Creating custom AI assistants without vendor dependency

Pros

  • +Open weights enable self-hosting and full data sovereignty
  • +Performance competitive with closed-source models
  • +Massive community ecosystem of fine-tunes and tools
  • +No per-token API costs when self-hosted

Cons

  • -Self-hosting requires significant GPU infrastructure for larger variants
  • -405B model is impractical for most organizations to deploy
  • -Licensing has some restrictions for very large-scale deployments
  • -Lacks native multimodal capabilities in base model

Pricing

Free to download and use. Self-hosting costs depend on hardware. Cloud hosting via providers like Together AI, Anyscale, or AWS Bedrock at $0.05-$2.00/1M tokens depending on model size.

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