Virtual Machines vs Physical Systems: Which Architecture Suits Your Application?

✅ Advantages of Virtual Machines

• Efficient Hardware Utilisation: Maximise the performance of high-end components by running several workloads on one system.
• Scalability: Quickly spin up new VMs as needed without purchasing new hardware.
• Cost-effective: Reduce software, hardware, power and cooling costs.
• Simplified Management: Manage all systems from a central interface.

❌ Disadvantages of Virtual Machines

• Resource Contention: Multiple VMs can compete for the same resources, leading to performance issues.
• Complex Setup: Requires knowledge of virtualization platforms and maintenance of virtual environments.
• Single Point of Failure: If the host system fails, all VMs go offline.

✅ Advantages of Multiple Systems

• Performance Isolation: No shared resources means more consistent performance for demanding applications.
• Improved Redundancy: One system can fail without affecting the others.
• Custom Hardware Per Task: Tailor each system with specific CPUs, GPUs, or storage based on workload.

❌ Disadvantages of Multiple Systems

• Higher Costs: More systems mean more hardware, energy use, and potential licensing costs.
• Space and Maintenance: Larger footprint and more points of failure to manage.
• Less Flexible: Scaling up requires physical installation and setup of additional machines.

1. AI Development Environment

• Single System: One powerful workstation running both Linux and Windows VMs to manage AI training, development, and testing environments.
• Multiple Systems: Two mid-range PCs (one Linux, one Windows) with dedicated workloads and consumer GPUs for cost-effective performance.

Verdict: A single system may save space and cost for light development; multiple systems offer better stability for concurrent, intensive use.

2. Simulation and Training

• Single System: Intel Xeon or AMD Threadripper workstation with a professional RTX A6000 card running multiple simulation environments as VMs.
• Multiple Systems: Two Intel Core i9 gaming PCs with NVIDIA RTX 4070s, each running individual simulations natively.

Verdict: For highly accurate, demanding simulations, pro hardware in a single system excels. For parallel training sessions or team use, multiple PCs may offer more flexibility.

3. Factory Automation

• Single System: Server running 10 – 20 VMs to control sensors, robotics, and databases across a factory floor.
• Multiple Systems: Edge network of small embedded PCs at each station performing local control tasks and reporting back to a central server.

Verdict: VM-based servers offer centralized control and ease of updates. Edge devices provide low-latency performance and system redundancy.

4. Retail POS and Inventory Management

• Single System: Rack-mounted server at HQ running VMs for multiple branch systems, POS terminals, and databases.
• Multiple Systems: Each branch has its own local system managing POS and inventory independently.
  
  

Verdict: VM approach is cost-effective and centralized, ideal for connected locations. Separate systems are better where connectivity is unreliable.

Edge Servers – High Performance Computers

Edge Devices – High Performance Mini PCs

IoT Controllers –
Ultra Compact Computers

Deep Learning / Training Computers and Servers

GPU/VPU Accelerated Computers