Medjed AI GPU Virtual Machines (VMs) provide high-performance computing resources with NVIDIA GPUs for accelerating AI/ML workloads, scientific computing, and data processing tasks. Our GPU VMs offer scalable, on-demand access to NVIDIA GPUs, enabling you to accelerate computationally intensive workloads by 10x to 100x compared to CPU-only systems.
Key Benefits
Section titled “Key Benefits”| Benefit | Description |
|---|---|
| Exceptional Performance | Accelerate AI/ML workloads with NVIDIA’s latest GPUs |
| Flexible Scaling | Choose from 1 to 8 GPUs per instance |
| Fast Deployment | Launch GPU VMs in approximately 10 milliseconds |
| Cost-Effective | Pay-as-you-go pricing with no long-term commitments |
| Secure Isolation | Isolated virtual environments with built-in security features |
| Easy Management | User-friendly dashboard for VM provisioning and monitoring |
GPU Types and Specifications
Section titled “GPU Types and Specifications”Medjed AI offers a range of NVIDIA GPUs to meet different workload requirements:
| GPU Model | GPU Memory | Typical Use Cases | Performance Profile |
|---|---|---|---|
| NVIDIA H100 | 80GB HBM3 | Large-scale AI training, deep learning inference | Highest performance for AI/ML workloads |
| NVIDIA A100 | 40GB/80GB SXM4 | AI/ML training, scientific computing, data analytics | Industry-standard for enterprise AI workloads |
| NVIDIA L40S | 48GB GDDR6 | AI content creation, graphics rendering, inference | Optimized for AI visualization and content generation |
| NVIDIA RTX A6000 | 48GB GDDR6 | Professional visualization, rendering, AI research | High-performance GPU for creative and research workloads |
Architecture
Section titled “Architecture”Each Medjed AI GPU VM consists of NVIDIA GPU(s), CPU, system memory, NVMe SSD storage, and a high-bandwidth network interface. VMs feature:
- Virtual Network Interface: Dedicated for each VM with up to 100 Gbps bandwidth
- Storage Options: NVMe SSD boot disk, additional data disks, and optional shared storage
Use Cases
Section titled “Use Cases”Medjed AI GPU VMs are ideal for:
- AI/ML Training: Train deep learning models faster with GPU acceleration
- Inference: Deploy and run AI models with low latency
- Scientific Computing: Accelerate complex simulations and calculations
- Data Analytics: Process large datasets with GPU-accelerated frameworks
- AI Content Creation: Generate AI-powered content and graphics
Getting Started
Section titled “Getting Started”To get started with Medjed AI GPU VMs:
- Evaluate Requirements: Determine the GPU type, memory, and resources needed for your workload
- Configure & Deploy: Use the Medjed AI dashboard to provision your GPU VM
- Connect: Access your VM via SSH and verify GPU availability with
nvidia-smi - Install Frameworks: Set up your preferred AI frameworks (PyTorch, TensorFlow, etc.)
For detailed instructions, see our QuickStart Guide.
Best Practices
Section titled “Best Practices”GPU Selection
Section titled “GPU Selection”- Match GPU model and memory to workload requirements
- Evaluate performance-to-cost ratio
Cost Optimization
Section titled “Cost Optimization”- Right-size instances to avoid over-provisioning
- Release unused VMs and storage to stop charges
- Monitor usage to optimize allocation
Performance Optimization
Section titled “Performance Optimization”- Use GPU-optimized frameworks and libraries
- Optimize batch sizes for efficient GPU memory usage
- Implement fast data loading techniques
- Consider distributed training for large models
Security
Section titled “Security”- Use SSH keys for secure access
- Configure firewalls to restrict network access
- Encrypt sensitive data at rest and in transit
- Keep OS and applications updated
Troubleshooting
Section titled “Troubleshooting”Common Issues
Section titled “Common Issues”| Issue | Resolution |
|---|---|
| GPU Not Detected | Verify GPU drivers with nvidia-smi |
| Low GPU Utilization | Optimize workload and batch processing |
| Memory Errors | Reduce batch size or use a GPU with more memory |
| Network Issues | Check firewall and network settings |
Next Steps
Section titled “Next Steps”- GPU VM Pricing: Review pricing models and costs
- Choosing the Right GPU: Guidance on selecting GPUs for your workload
- Connecting to GPU VMs: Detailed access instructions
- Support Portal: Get assistance from our team
Last updated: 2026-01-13