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Llama 3.1

Llama 3.1 is Meta's 8B, 70B and 405B family with 128K context, eight supported languages and downloadable weights.

Large Language ModelText Open Source
In plain English

What is this model and why does it matter?

Llama 3.1 is Meta's 8B/70B/405B text-model family with 128K context, multilingual support and tool-use capabilities.

Self-hosted assistantsRAGMultilingual chatCodingTool useFine-tuning
Model overview

Llama 3.1: features, use cases and important details

Llama 3.1 is Meta’s July 2024 text-model family spanning 8B, 70B and 405B sizes.

Verified model facts

Meta introduced a 128K context window, support for eight languages and stronger tool-use, reasoning and coding capabilities across the family.

Current status

The official weights remain available under the Llama 3.1 Community License and are widely deployable through local and third-party infrastructure.

Best fit

It is well suited to RAG, multilingual assistants, coding, fine-tuning and synthetic-data generation.

Limitations

The family has a December 2023 static training cutoff, the custom license requires review and large variants are expensive to serve.

Get started

How to use this model

  1. Choose 8B, 70B or 405B and base vs Instruct.
  2. Accept the Llama 3.1 Community License.
  3. Download official Meta weights.
  4. Use a compatible serving stack.
  5. Evaluate newer Llama generations before a new production rollout.
Copy and try

Example prompts

  • Summarize this long multilingual report.
  • Use the provided tools to answer this request.
  • Generate synthetic training data for this domain.
Capabilities

What it can do

  • 128K context
  • 8B/70B/405B sizes
  • Multilingual text
  • Tool use
  • Fine-tuning
  • Synthetic data
Best for

Practical use cases

  • Enterprise self-hosting
  • RAG
  • Coding assistants
  • Multilingual agents
  • Research
Pricing

What does it cost?

Downloadable Meta weights; no universal Meta token price applies because hosting is provided locally or through third parties.

Simple summaryServing cost ranges dramatically from 8B to 405B and depends on hosting provider, precision and hardware.

What stands out

  • Three major size tiers
  • 128K context
  • Large ecosystem
  • Tool-use support

Things to consider

  • Custom community license
  • Static December 2023 cutoff
  • 405B requires extreme infrastructure
Limitations

Important restrictions and trade-offs

  • Can hallucinate
  • No direct Meta token price
  • Base and Instruct checkpoints behave differently
SimplifyAITools verdict

Our editorial take

An important open-weight family for self-hosting and customization, though new projects should compare later Llama generations.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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