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Llama 3 8B

Deprecated
llama3-8b-8192
Try it in Playground
TOKEN SPEED
1,250 tps
Powered bygroq
INPUT
Text
OUTPUT
Text
CAPABILITIES
Tool Use, JSON Object Mode
Meta logoMeta
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

QUANTIZATION

This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.

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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