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Llama 3 8B

Deprecated
llama3-8b-8192
Try it in Playground
TOKEN SPEED
1250 tps
INPUT
Text
OUTPUT
Text
CAPABILITIES
Tool Use, JSON Mode
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model card

Llama-3-8B-8192 delivers great performance with industry-leading speed and cost-efficiency on Groq hardware. This model stands out as one of the most economical options in our lineup while maintaining impressive throughput, making it perfect for high-volume applications where both speed and cost matter. Despite its compact 8B parameter size, it maintains strong language capabilities for handling a wide range of tasks with remarkable efficiency.

PRICING

Input
$0.05
20M / $1
Output
$0.08
13M / $1

LIMITS

CONTEXT WINDOW
8,192
MAX OUTPUT TOKENS
8,192

Key Technical Specifications

Model Architecture

Built on Meta's Llama 3 architecture, this 8B parameter model features Grouped-Query Attention (GQA) for enhanced inference scalability. It has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align outputs with human preferences for helpfulness and safety.

Performance Metrics

The model demonstrates outstanding performance across a range of benchmarks, significantly outperforming previous generation models of similar size:
  • MMLU (Massive Multitask Language Understanding): 66.6% accuracy
  • HumanEval (code generation): 62.2% pass@1
  • MATH (mathematical problem solving): 30.0% sympy intersection score
  • GSM-8K (Multilingual Grade School Math): 79.6% exact match

Use Cases

High-Volume Processing
Ideal for applications requiring rapid processing of large volumes of text with minimal latency and cost:
  • Real-time chat applications with high user concurrency
  • Automated customer support systems requiring immediate responses
  • High-throughput data processing and classification pipelines
Cost-Sensitive Applications
Perfect for scenarios where processing costs need to be minimized without compromising on speed or quality:
  • Large-scale document processing and information extraction
  • Continuous monitoring and analysis of text data streams
  • Educational platforms serving multiple users simultaneously
Real-Time Applications
Excels in use cases where immediate responses are critical to user experience:
  • Interactive chatbots requiring sub-second response times
  • Live assistance tools for content creation and editing
  • Real-time language translation services

Best Practices

  • Optimize Prompts: Design clear, concise instructions to maximize efficiency and minimize token usage
  • Prioritize Throughput: Structure your application to take full advantage of the model's exceptional speed
  • Implement Batching: Group similar requests together to maximize cost efficiency and processing speed

Get Started with Llama-3-8B-8192

Experience the perfect balance of speed, cost, and capability with llama-3-8b-8192 with Groq speed:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="llama3-8b-8192",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

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