GTC 2026: With Groq 3 LPX, Nvidia adds dedicated inference hardware to its platform for the first time

Introduction

In this tutorial, you'll learn how to set up and deploy a custom inference workload using Nvidia's Vera Rubin platform, specifically leveraging the new Groq 3 LPX inference chips. This platform represents a significant shift in Nvidia's strategy, offering dedicated hardware for inference workloads that were previously handled on general-purpose GPUs. The Vera Rubin platform includes custom CPU racks, dedicated inference chips, a new storage architecture, an inference operating system, and agent security software. We'll focus on creating a simple but realistic inference pipeline that demonstrates how to utilize this new hardware.

Prerequisites

• Basic understanding of machine learning and neural networks
• Access to a system with Nvidia GPUs (for development/testing)
• Python 3.8 or higher installed
• Access to the Vera Rubin platform or a simulation environment
• Basic knowledge of Docker and containerization

Why these prerequisites? Understanding ML concepts will help you grasp how inference works, while Docker knowledge is essential for deploying applications in the Vera Rubin environment. The GPU access allows for local testing of inference logic before deployment.

Step-by-Step Instructions

1. Set up your development environment

First, create a virtual environment and install the necessary dependencies for working with the Vera Rubin platform.

python -m venv vera_env
source vera_env/bin/activate  # On Windows: vera_env\Scripts\activate
pip install nvidia-ml-py3 torch torchvision transformers

Why? This creates an isolated environment for our project, preventing conflicts with other Python packages. We're installing the Nvidia Management Library, PyTorch, and Hugging Face Transformers which are essential for working with inference workloads.

2. Create a sample model for inference

Next, we'll create a simple text classification model that we'll later deploy on the Vera Rubin platform.

import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel

class TextClassifier(nn.Module):
    def __init__(self, model_name='bert-base-uncased', num_labels=2):
        super().__init__()
        self.bert = AutoModel.from_pretrained(model_name)
        self.dropout = nn.Dropout(0.3)
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
    
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output
        output = self.dropout(pooled_output)
        return self.classifier(output)

# Initialize model and tokenizer
model = TextClassifier()
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

Why? This creates a BERT-based classifier that we can use to demonstrate inference. The model will be exported and deployed on the Vera Rubin platform's dedicated inference chips.

3. Export the model for inference

Before deploying to the Vera Rubin platform, we need to convert our model to a format compatible with the inference hardware.

import torch.onnx as onnx

# Dummy input for export
dummy_input = torch.randn(1, 128)
attention_mask = torch.ones(1, 128)

# Export to ONNX format
onnx.export(
    model,
    (dummy_input, attention_mask),
    "text_classifier.onnx",
    export_params=True,
    opset_version=13,
    do_constant_folding=True,
    input_names=['input_ids', 'attention_mask'],
    output_names=['output'],
    dynamic_axes={
        'input_ids': {0: 'batch_size', 1: 'sequence_length'},
        'attention_mask': {0: 'batch_size', 1: 'sequence_length'},
        'output': {0: 'batch_size'}
    }
)

Why? ONNX (Open Neural Network Exchange) is a standard format that allows models to be run across different platforms, including the Vera Rubin platform's dedicated inference chips. This export process prepares the model for deployment.

4. Configure the inference environment

Create a Dockerfile that will containerize our inference workload for deployment on the Vera Rubin platform.

FROM nvidia/cuda:12.1.0-devel-ubuntu20.04

# Install Python and dependencies
RUN apt-get update && apt-get install -y python3 python3-pip

# Set working directory
WORKDIR /app

# Copy requirements and install
COPY requirements.txt .
RUN pip3 install -r requirements.txt

# Copy model and application code
COPY text_classifier.onnx .
COPY app.py .

# Expose port
EXPOSE 8000

# Run application
CMD ["python3", "app.py"]

Why? Docker containers ensure that our inference workload runs consistently across different environments. The Vera Rubin platform uses containerized deployments for managing inference workloads.

5. Implement the inference service

Create the application code that will serve our model using the Vera Rubin platform's inference capabilities.

import torch
import torch.onnx
from flask import Flask, request, jsonify
import numpy as np
from transformers import AutoTokenizer

app = Flask(__name__)

# Load the ONNX model
session = torch.onnx.load("text_classifier.onnx")

# Initialize tokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    text = data['text']
    
    # Tokenize input
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
    
    # Run inference
    with torch.no_grad():
        outputs = model(inputs['input_ids'], inputs['attention_mask'])
        predictions = torch.nn.functional.softmax(outputs, dim=1)
    
    return jsonify({
        'predictions': predictions.tolist(),
        'sentiment': 'positive' if predictions[0][1] > 0.5 else 'negative'
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)

Why? This creates a REST API endpoint that can be called to perform inference on text. The Vera Rubin platform will manage the deployment of this service on the Groq 3 LPX chips.

6. Deploy to the Vera Rubin platform

Finally, we'll simulate how to deploy our inference workload to the Vera Rubin platform using the platform's deployment tools.

# This would typically be run in the Vera Rubin platform environment
import subprocess

# Build Docker image
build_cmd = "docker build -t inference-service ."
subprocess.run(build_cmd.split())

# Push to platform registry (simulated)
registry_cmd = "docker tag inference-service registry.vera-rubin.com/inference-service:latest"
subprocess.run(registry_cmd.split())

# Deploy to Vera Rubin platform
# This would be handled through the platform's API or CLI
print("Deployment to Vera Rubin platform complete")

Why? The Vera Rubin platform's deployment process involves containerizing applications and managing them through a dedicated orchestration system. This step shows how the inference workload would be deployed to the platform's dedicated hardware.

Summary

In this tutorial, you've learned how to prepare a machine learning model for deployment on Nvidia's new Vera Rubin platform, specifically utilizing the Groq 3 LPX inference chips. You've created a text classification model, exported it to ONNX format, containerized it with Docker, and simulated the deployment process to the Vera Rubin platform. This demonstrates how organizations can leverage dedicated inference hardware for more efficient and scalable inference workloads.

The Vera Rubin platform represents a significant evolution in AI infrastructure, moving from general-purpose GPU workloads to specialized inference hardware. This approach will enable more efficient processing of large language models and other AI workloads, reducing latency and increasing throughput compared to traditional GPU-based inference.