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Meta Llama 3
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Meta Llama 3 Verified Tool

Meta Llama 3 is a generation of open-weight language models from Meta for chat, text generation, research, fine-tuning, and application development; newer Llama releases are now also available.

Last Update: August 20, 2026

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Starting price Free model weights under license

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Meta Llama 3 is a generation of open-weight language models from Meta for chat, text generation, research, fine-tuning, and application development; newer Llama releases are now also available.

Developers read the exact model card and license, select a suitable current checkpoint, isolate untrusted inputs, evaluate safety and bias, secure deployment, and disclose model and version in consequential uses.

Model weights are available without a subscription under Meta’s applicable community license; compute, hosting, storage, fine-tuning, safety, and engineering are separate costs.

Open-weight models can hallucinate, generate harmful content, leak training-like data, or be deployed without safeguards. License compliance, evaluation, moderation, security, provenance, and human oversight are essential.

F.A.Q (3)

Meta Llama 3 is a generation of open-weight language models from Meta for chat, text generation, research, fine-tuning, and application development; newer Llama releases are now also available.

Developers read the exact model card and license, select a suitable current checkpoint, isolate untrusted inputs, evaluate safety and bias, secure deployment, and disclose model and version in consequential uses.

Verified pricing: Free model weights under license. Model weights are available without a subscription under Meta’s applicable community license; compute, hosting, storage, fine-tuning, safety, and engineering are separate costs.

Pros and Cons

Pros

  • Meta released both pretrained and instruction-tuned Llama 3 models
  • The original family included practical 8B and 70B parameter sizes
  • Model weights enabled deployment outside a single hosted API
  • Grouped-query attention improved inference efficiency
  • A 128;000-token vocabulary made tokenization more efficient than Llama 2
  • Llama 3 was trained on more than fifteen trillion tokens
  • Training data included substantially more code than the prior generation
  • Instruction tuning improved reasoning; coding; and prompt following
  • Developers could fine-tune the base model for specialized domains
  • Broad cloud support included AWS; Azure; Google Cloud; and other platforms
  • Hugging Face distribution simplified community experimentation
  • Torchtune supplied PyTorch-native fine-tuning recipes
  • Llama Guard 2 offered a configurable safety-classification layer
  • Code Shield helped filter insecure generated code
  • CyberSecEval 2 expanded cybersecurity risk evaluation
  • The later Llama 3.1 release added multilingual support; tool use; and a 128K context window

Cons

  • The original April 2024 Llama 3 release is no longer Meta's newest model generation
  • Its first models supported only an 8;192-token training context
  • Meta expected weaker performance outside English in the original release
  • Even the 8B model needs meaningful memory and compute for local inference
  • The 70B and 405B variants require costly server-grade hardware
  • Quantization reduces memory use but can degrade output quality
  • Llama's community license is not identical to a standard open-source license
  • Very large services can trigger additional license conditions
  • Model weights do not include a complete production safety system
  • Developers must add authentication; moderation; logging; and abuse prevention
  • Llama Guard can miss harmful content or over-block benign requests
  • Llama 3 can hallucinate facts and fabricate citations
  • Generated code can contain vulnerabilities despite Code Shield
  • Public-data training raises unresolved privacy and copyright concerns
  • Self-hosting transfers patching and incident response to the deployer
  • New applications should compare newer Llama releases before choosing Llama 3

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Overview

Pricing
Free

Applicable Tasks

AI development pretrained models instruction tuning AI model customization high-scale AI training complex problem solving

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  • Commercial usage rights vary by plan, provider terms and jurisdiction.
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