---
title: "Groq: Lightning-Fast LLM Inference with Llama 3.3 and Mixtral in Pixeltable"
date: "2025-05-20"
author: "Pixeltable Team"
tags:
  - Groq
  - LPU
  - Fast Inference
  - Llama 3.3
  - Mixtral
  - AI Integration
  - Pixeltable
description: "Build blazing-fast AI applications with Groq's custom LPU hardware. Learn how to use Llama 3.3, Mixtral, and other models with Pixeltable for sub-second response times and production-ready orchestration."
url: "https://pixeltable.com/blog/groq-fast-inference-pixeltable"
---

# Groq: Lightning-Fast LLM Inference with Llama 3.3 and Mixtral in Pixeltable

Groq's custom Language Processing Units (LPUs) deliver the fastest LLM inference available, with response times measured in milliseconds. Combined with Pixeltable's declarative infrastructure, you can build real-time AI applications that feel instantaneous.

 
## Groq: The Speed Revolution in LLM Inference

 
**Groq** has redefined what's possible with LLM inference speed. Their custom-designed **LPU** architecture delivers tokens at rates that make traditional GPU inference feel sluggish.

 
 
When combined with Pixeltable's [declarative infrastructure](/blog/unified-multimodal-ai-infrastructure-pixeltable), you get both world-class speed and enterprise-grade data management.

 
## Speed Comparison

 
| Provider | Model | Typical Latency |
| --- | --- | --- |
| Groq | Llama 3.3 70B | ~200ms |
| OpenAI | GPT-4o | ~2-5s |
| Anthropic | Claude 3.5 | ~2-4s |

 
## Available Models

 

 - **Llama 3.3 70B Versatile:** Best for general tasks

 - **Mixtral 8x7B:** Efficient mixture of experts

 - **Gemma 2 9B:** Google's efficient open model

 - **Llama Guard 3:** Content moderation and safety

 

 
## Getting Started

 
```bash
# Install required packages
pip install pixeltable groq

# Set your Groq API key
export GROQ_API_KEY="your-api-key-here"
```

 
## Basic Chat Completions

 
```python
import pixeltable as pxt
from pixeltable.functions import groq

pxt.drop_dir('groq_demo', force=True)
pxt.create_dir('groq_demo')

t = pxt.create_table('groq_demo.chat', {'input': pxt.String})

messages = [{'role': 'user', 'content': t.input}]
t.add_computed_column(output=groq.chat_completions(
 messages=messages,
 model='llama-3.3-70b-versatile',
 model_kwargs={'max_tokens': 300, 'temperature': 0.7}
))

t.add_computed_column(response=t.output.choices[0].message.content)

t.insert([{'input': 'How many islands are in the Aleutian chain?'}])
t.select(t.input, t.response).show()
```

 
## Real-Time Content Classification

 
```python
import pixeltable as pxt
from pixeltable.functions import groq

content = pxt.create_table('groq_demo.moderation', {'text': pxt.String})

content.add_computed_column(
 classification=groq.chat_completions(
 messages=[{
 'role': 'system',
 'content': 'Classify the text: positive, negative, neutral, spam. Respond with just the category.'
 }, {
 'role': 'user',
 'content': content.text
 }],
 model='llama-3.3-70b-versatile',
 model_kwargs={'max_tokens': 10, 'temperature': 0}
 ).choices[0].message.content
)

content.insert([
 {'text': 'This product is amazing! Best purchase ever.'},
 {'text': 'Click here for FREE money!!!'}
])

content.select(content.text, content.classification).show()
```

 
## Cost Efficiency

 
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
| --- | --- | --- |
| Llama 3.3 70B | $0.59 | $0.79 |
| Mixtral 8x7B | $0.24 | $0.24 |
| Gemma 2 9B | $0.20 | $0.20 |

 
## Next Steps

 

 - [Building AI Agents](/blog/practical-guide-building-agents)

 - [Compare Providers](/blog/multi-provider-ai-strategy-comparison-guide)

 - [All Integrations](https://docs.pixeltable.com/integrations/frameworks)

 

 
## Resources

 

 - [Pixeltable Groq Documentation](https://docs.pixeltable.com/howto/providers/working-with-groq)

 - [Groq Documentation](https://console.groq.com/docs)

 - [Join our Discord Community](https://discord.com/invite/QPyqFYx2UN)