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Llama3-70B-8192

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Llama 3.0 70B on Groq offers a balance of performance and speed as a reliable foundation model that excels at dialogue and content-generation for tasks requiring smaller context windows. While newer models have since emerged, Llama 3.0 70B remains production-ready and cost-effective with fast, consistent outputs via Groq API.

Key Technical Specifications

Model Architecture

A 70 billion parameter transformer that includes enhanced attention mechanisms and optimized training objectives. It offers solid instruction-following capabilities and reduced hallucinations.

Performance Metrics

The model demonstrates solid performance across various benchmarks:
  • MMLU (5-shot): 79.5% accuracy, showing strong general knowledge
  • GSM-8K (8-shot, CoT): 93.0% accuracy in mathematical reasoning
  • HumanEval (0-shot): 81.7% pass rate in code generation

Technical Details

FEATUREVALUE
Context Window (Tokens)8,192
Max Output Tokens-
Max File Size-
Token Generation Speed~330 tps
Input Token Price$0.59/1M tokens
Output Token Price$0.79/1M tokens
Tool UseSupported
JSON ModeSupported
Image SupportNot Supported

Use Cases

Dialogue Applications
Ideal for building reliable conversational experiences with consistent outputs:
  • Customer support and service chatbots
  • Interactive assistants and guides
  • Educational dialogue systems
  • Conversational interfaces for applications
Content Generation
Excels at creating high-quality content with a balance of creativity and accuracy:
  • Marketing and promotional content
  • Documentation and technical writing
  • Creative writing and storytelling
  • Content adaptation and summarization

Best Practices

  • Structure your prompts: Break complex tasks into clear steps for more reliable outputs
  • Enable JSON mode: For generating structured data and maintaining consistent output formats
  • Include examples: Add sample outputs or specific formats to guide complex generations

Get Started with llama3-70b

Experience the versatile llama3-70b-8192 with Groq speed now:

pip install groq
1from groq import Groq
2client = Groq()
3completion = client.chat.completions.create(
4    model="llama3-70b-8192",
5    messages=[
6        {
7            "role": "user",
8            "content": "Explain why fast inference is critical for reasoning models"
9        }
10    ]
11)
12print(completion.choices[0].message.content)