LLaMA-3.2-1B-Preview

LLaMA-3.2-1B-Preview is one of the fastest models on Groq, making it perfect for cost-sensitive, high-throughput applications. With just 1.23 billion parameters and a 128K context window, it delivers near-instant responses while maintaining impressive accuracy for its size. The model excels at essential tasks like text analysis, information retrieval, and content summarization, offering an optimal balance of speed, quality and cost. Its lightweight nature translates to significant cost savings compared to larger models, making it an excellent choice for rapid prototyping, content processing, and applications requiring quick, reliable responses without excessive computational overhead.

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Key Technical Specifications

Model Architecture

LLaMA-3.2-1B-Preview is an auto-regressive language model built upon Meta's LLaMA-3.2 architecture. Utilizing an optimized transformer architecture, it supports text and code generation.

Performance Metrics

The model demonstrates impressive performance across key benchmarks:

MMLU: 32.2% accuracy (#5 shot)
Arc-Challenge: 32.8% accuracy (#25 shot)
SQuAD: 49.2% accuracy (#1 shot)

Technical Details

FEATURE	VALUE
Context Window (Tokens)	128k
Max Output Tokens	8k
Max File Size	-
Token Generation Speed	~3100 tps
Input Token Price	$0.04/1M tokens
Output Token Price	$0.04/1M tokens
Tool Use
JSON Mode
Image Support

Use Cases

Real-Time Applications

Perfect for applications requiring instant responses and high throughput.

Live chat systems with sub-100ms response times
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.

Batch processing of documents and articles
Large-scale content summarization
Automated data extraction and analysis

Best Practices

Enable JSON mode: For generating structured data or when you need outputs in a specific format
Use tool use: For tasks that require external tools or services to generate responses

Get Started with LLaMA-3.2-1B-Preview

Experience lightning-fast text analysis and content generation with llama-3.2-1b-preview with Groq speed now:

shell

pip install groq

Python

from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="llama-3.2-1b-preview",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)