Can Remote Desktop Infrastructure Keep Up with AI Workloads?

How AI Workloads Challenge Traditional Remote Desktop Infrastructure

Established systems are optimised for traditional enterprise workloads like project management and ERP systems, which need moderate CPU and memory. But AI workloads differ fundamentally from standard desktop applications. They need more room and capacity to run for their operating loads. 

This increase in computing power requires more resources, including electricity and cooling systems, and generates more heat while increasing volatility. AI tasks can push your existing RDS architecture to its limits, especially in mid-to-large-scale or GPU‑dependent AI tasks.

Increasing the scale of your infrastructure is the natural solution, but that alone might not be enough. AI workloads best function on an RDS infrastructure that’s quick to respond and adapt to dynamic, unpredictable demands. 

Here’s how AI tasks challenge current RDS deployments:

Over-Reliance on GPUs

Training and inference are two fundamental processes in machine learning (ML) that AI models depend on. These heavily rely on graphics processing units, or GPUs, which are optimised for parallel computations required for deep learning. You may need to invest in a GPU-accelerated remote desktop to keep up with your business’ growing workloads. 

High Data Throughput

AI models use large datasets, and this means constantly moving data between memory, storage, and compute nodes. These workloads may not function effectively without the right supportive network infrastructure.

Latency Sensitivity

Chatbots and real-time recognition models need to deliver instant and high-quality responses for user satisfaction. The quality of inference tasks greatly depends on latency sensitivity. Caching and hardware acceleration can lower latency and improve inference performance.

It’s essential that you overcome these three key challenges if you’re keen to experiment with AI tasks on your current RDS architecture. 

Can RDS Evolve to Meet AI Demands?

Many of the existing RDS platforms aren’t yet optimised for AI. But with careful planning and adaptation, your RDS architecture can meet the demands of growing AI workloads in the future.

Here’s how the technology is shaping up:

GPU Virtualisation

More RDS providers are working on stable GPU passthrough and virtual GPU solutions.

A GPU passthrough is a virtualisation technique that allows a VM to directly access a physical GPU and support the high performance of graphics-intensive applications. These advanced solutions allow AI workloads to run inside remote desktop sessions.

Smarter Resource Orchestration

Advances in hypervisors and RDS management now make it possible to allocate compute resources dynamically to AI tasks.

Integration with AI Clouds

RDS can act as a gateway, letting users work through virtual desktops while the heavy processing happens on AI-optimised backend systems.