Llama-3-8B-8192
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.
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
Technical Details
| FEATURE | VALUE |
|---|---|
| Context Window (Tokens) | 8,192 |
| Max Output Tokens | - |
| Max File Size | - |
| Token Generation Speed | 1250 tps |
| Input Token Price | $0.05/1M tokens |
| Output Token Price | $0.08/1M tokens |
| Tool Use |  |
| JSON Mode | |
| Image Support |  |
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:
pip install groq1from groq import Groq
2client = Groq()
3completion = client.chat.completions.create(
4 model="llama3-8b-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)