Deployment Overview

Voxtral can be deployed in various configurations depending on your requirements. This guide covers production deployment strategies for both the 3B and 24B parameter models.

Hardware Requirements

Minimum Requirements

• Voxtral 3B: 8GB GPU memory, 16GB system RAM
• Voxtral 24B: 48GB GPU memory, 64GB system RAM
• CUDA-compatible GPU (recommended: A100, H100, RTX A6000)
• Fast SSD storage for model files (10GB+)

Recommended Production Setup

• Multiple GPU setup for horizontal scaling
• Load balancer for request distribution
• Redis or similar for session management
• Monitoring and logging infrastructure

Docker Deployment

The easiest way to deploy Voxtral in production is using Docker containers:

# Dockerfile example
FROM nvidia/cuda:11.8-runtime-ubuntu20.04

RUN apt-get update && apt-get install -y python3 python3-pip
RUN pip3 install vllm mistral-common

EXPOSE 8000

CMD ["vllm", "serve", "mistralai/voxtral-24b", "--host", "0.0.0.0"]

Kubernetes Deployment

For scalable production deployments, use Kubernetes with GPU support:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: voxtral-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: voxtral
  template:
    metadata:
      labels:
        app: voxtral
    spec:
      containers:
      - name: voxtral
        image: voxtral:latest
        resources:
          limits:
            nvidia.com/gpu: 1
          requests:
            memory: "32Gi"
            cpu: "8"

Load Balancing

Implement load balancing for high availability and performance:

NGINX Configuration

upstream voxtral_backend {
    server voxtral-1:8000;
    server voxtral-2:8000;
    server voxtral-3:8000;
}

server {
    listen 80;
    location / {
        proxy_pass http://voxtral_backend;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

Performance Optimization

Model Optimization

• Use tensor parallelism for large models
• Implement model quantization for reduced memory usage
• Enable KV cache for faster inference
• Configure appropriate batch sizes

System Optimization

• Tune GPU memory allocation
• Optimize CPU scheduling
• Configure appropriate swap settings
• Use fast storage for model checkpoints

Monitoring and Logging

Essential monitoring for production deployments:

Key Metrics

• Request latency and throughput
• GPU utilization and memory usage
• Model accuracy and error rates
• System resource consumption

Logging Setup

# Example logging configuration
import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('voxtral.log'),
        logging.StreamHandler()
    ]
)

Security Considerations

• Implement API authentication and rate limiting
• Use HTTPS for all communications
• Sanitize audio input files
• Regular security updates and patches
• Network segmentation and firewall rules

Backup and Recovery

• Regular model checkpoint backups
• Configuration file versioning
• Database backup strategies
• Disaster recovery procedures

Troubleshooting

Common Issues

• Out of memory errors: Reduce batch size or use model sharding
• Slow inference: Check GPU utilization and optimize hardware
• Connection issues: Verify network configuration and firewall rules
• Model loading failures: Check storage permissions and available space

Need help with implementation? Check our FAQ for common questions and solutions.