Llama 3.1 8B
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Llama 3.1 8B on Groq provides low-latency, high-quality responses suitable for real-time conversational interfaces, content filtering systems, and data analysis applications. This model offers a balance of speed and performance with significant cost savings compared to larger models. Technical capabilities include native function calling support, JSON mode for structured output generation, and a 128K token context window for handling large documents.
Key Technical Specifications
Model Architecture
Built upon Meta's Llama 3.1 architecture, this model utilizes an optimized transformer design with 8 billion parameters. It incorporates Grouped-Query Attention (GQA) for improved inference scalability and efficiency. The model has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance response accuracy.
Performance Metrics
Despite its compact size, the model demonstrates strong performance across key benchmarks, making it suitable for many practical applications:
- MMLU (Massive Multitask Language Understanding): 69.4% accuracy
- HumanEval (code generation): 72.6% pass@1
- MATH (mathematical problem solving): 51.9% sympy intersection score
- TriviaQA-Wiki (knowledge retrieval): 77.6% exact match
Technical Details
| FEATURE | VALUE |
|---|---|
| Context Window (Tokens) | 128k |
| Max Output Tokens | 8,192 |
| Max File Size | N/A |
| Token Generation Speed | ~750 tps |
| Input Token Price | $0.05 per 1M tokens |
| Output Token Price | $0.08 per 1M tokens |
| Tool Use |  |
| JSON Mode | |
| Image Support |  |
Use Cases
Real-Time Applications
Perfect for applications requiring instant responses and high throughput:
- Real-time content moderation and filtering
- Interactive educational tools and tutoring systems
- Dynamic content generation for social media
High-Volume Processing
Ideal for processing large amounts of data cost-effectively:
- Large-scale content summarization
- Automated data extraction and analysis
- Bulk metadata generation and tagging
Best Practices
- Leverage the context window: Use the large context window to maintain context for large-scale processing
- Simplify complex queries: Break down multi-part questions into clear, small steps for more reliable reasoning
- Enable JSON mode: For generating structured data or when you need outputs in a specific format
- Include examples: Add sample outputs or specific formats to guide the model into specific output structures
Get Started with llama 3.1 8b instant
Experience the capabilities of llama-3.1-8b-instant with Groq speed:
pip install groq1from groq import Groq
2client = Groq()
3completion = client.chat.completions.create(
4 model="llama-3.1-8b-instant",
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)