The rapid advancement of AI models has created an interesting challenge: powerful models require substantial computing resources, yet many of us have multiple computing devices that spend much of their time idle. Technologies like EXO attempt to bridge this gap by creating local computing clusters from your existing devices. But how practical is this approach, and what should you understand before attempting to build your own AI computing cluster?

Hardware Complementarity in AI Clusters

The foundational concept behind distributed AI clusters is hardware complementarity—the strategic combination of different computing devices to achieve better performance than any single device could provide alone. This might mean combining:

• A gaming PC with powerful GPU capabilities
• A MacBook with Apple Silicon
• A workstation with strong CPU performance
• Mini computers like Raspberry Pi

Each device brings different strengths to the cluster. GPUs excel at parallel processing tasks, while some CPUs might offer advantages in specific computational patterns. In theory, proper orchestration allows these diverse capabilities to complement each other.

However, creating true complementarity requires sophisticated resource management. The system must understand each device’s strengths and weaknesses, then distribute workloads accordingly. This remains one of the most challenging aspects of distributed AI computing.

Memory Requirements and Their Implications

One of the most significant limitations in current distributed AI approaches relates to memory requirements. As demonstrated in the video, each node in the cluster needs enough memory to load the entire AI model, even if it’s only processing a portion of the workload.

For example, if a language model requires 6GB of RAM to run, every device in your cluster needs at least that much available memory. This creates a practical floor for participation—devices that fall below the memory threshold simply can’t contribute, regardless of their other capabilities.

This limitation has important implications:

• You can’t overcome individual device memory limitations by adding more devices
• Adding very small devices (like basic Raspberry Pis) may not be feasible for larger models
• The cluster’s capabilities are bounded by what the smallest device can handle

Understanding these memory constraints is crucial when planning a distributed AI system. The ideal scenario combines devices that each have sufficient memory while bringing complementary processing strengths.

Network Communication Overhead

When running AI workloads across multiple devices, data must flow between them. This network communication introduces overhead that can significantly impact performance. Several factors influence this overhead:

• Connection speed and type: Ethernet connections typically provide lower latency than WiFi
• Physical proximity: Devices physically closer to each other generally experience less network latency
• Data transfer volume: The amount of information that must be exchanged between devices
• Synchronization requirements: How often devices need to coordinate their activities

In some cases, the overhead of network communication can negate the benefits of adding additional devices. This is particularly true when combining many low-powered devices, where the coordination costs may outweigh the processing gains.

Practical Use Cases for Consumers

Despite these limitations, several practical use cases make distributed AI processing appealing:

• Home office setups where you might combine a work laptop with a personal desktop
• Creative environments where multiple computing devices already exist for different purposes
• Small development teams looking to pool local resources for AI testing
• Educational settings where creating a learning cluster can demonstrate distributed computing principles

The key is matching the approach to appropriate expectations. A distributed cluster might not replace a high-end dedicated machine, but it can potentially deliver better performance than your existing devices operating independently.

In the demonstration, combining two nodes achieved a more than 50% increase in performance—from 2.1 to 3.6 tokens per second. While these numbers will vary based on specific hardware configurations, they illustrate the potential benefits of the approach.

As distributed AI inference technologies mature, we’re likely to see improvements that address current limitations. Future iterations might better handle memory constraints or reduce network overhead. For now, understanding both the potential and limitations of these systems will help you make informed decisions about whether and how to implement them in your own environment.

To see exactly how to implement these concepts in practice, watch the full video tutorial on YouTube. I walk through each step in detail and show you the technical aspects not covered in this post. If you’re interested in learning more about AI engineering, join the AI Engineering community where we share insights, resources, and support for your learning journey.