AI cloud generally refers to cloud computing infrastructure and services that are purpose-built or optimized to support AI and machine learning workloads. Depending on the provider, offerings may include GPU and other accelerator instances, pre-configured ML frameworks, managed training and inference services, and high-bandwidth networking and storage designed to efficiently support AI workloads.
Compared with general-purpose cloud infrastructure, AI-focused cloud offerings place greater emphasis on specialized accelerators, high-performance networking, large-scale parallel processing, and the ability to provision resources for demanding training and inference workloads. Depending on the workload, these environments can scale from individual accelerators to large clusters supporting distributed AI workloads.
What Are the Benefits of AI Cloud?
Organizations may adopt AI cloud services for several reasons:
- Access without capital investment. Teams can access high-end GPUs and accelerators without the upfront cost of purchasing and housing the hardware themselves.
- Elastic scalability. Training workloads can scale up dramatically for a finite period and scale back down once a model is trained, avoiding the cost of owning fixed capacity for peak demand.
- Faster time to deployment. Pre-configured environments, managed ML pipelines, and available capacity can reduce the lead time typically associated with procuring and provisioning specialized hardware.
- Access to the latest hardware. Providers may offer access to newer generations of GPUs and accelerators, giving customers access to performance improvements without their own hardware refresh cycles.
What Are the Challenges of AI Cloud?
AI cloud adoption also introduces considerations that data center and IT teams potentially need to plan around:
- Cost at scale. Sustained, large-scale training or inference workloads can become more expensive in the cloud over time than owning equivalent on-premises capacity, prompting some organizations to pursue cloud repatriation for stable, predictable workloads.
- Data gravity and latency. Moving large training datasets into the cloud, or moving inference workloads closer to where data is generated, can introduce latency and egress costs that affect architecture decisions.
- Capacity constraints. Demand for high-end GPU capacity has at times outpaced supply, meaning the desired instance types are not always immediately available in every region or from every provider.
- Vendor and workload placement decisions. With AI cloud and on-premises AI infrastructure all viable options, organizations must continuously evaluate where each workload is best run based on cost, performance, compliance, and data location requirements.
Where On-Premises Infrastructure Still Fits
Even as organizations adopt AI cloud services, many use hybrid strategies in which sensitive data, latency-critical inferencing, or steady-state workloads run on-premises or in colocation facilities. This makes it essential for data center teams to understand the power, cooling, and space implications of supporting AI-ready racks locally, alongside whatever workloads run in the AI cloud.
Manage Your Hybrid AI Infrastructure with DCIM Software
As AI workloads span cloud, neocloud, and on-premises environments, data center teams need visibility into the physical infrastructure supporting the portion of AI workloads that remain local. Data Center Infrastructure Management (DCIM) software provides that visibility for on-premises and colocation facilities.
DCIM software helps organizations:
- Determine AI readiness. Use intelligent capacity search and what-if analysis to identify whether existing racks and infrastructure can support AI workloads before committing budget to cloud services or new construction.
- Plan power and cooling for high-density deployments. Model power distribution down to the individual rack and device level and model liquid cooling infrastructure to safely support the density and thermal requirements that AI hardware requires.
- Track hybrid dependencies. Maintain an accurate inventory of on-premises AI infrastructure and integrate with supported cloud and virtualization platforms to improve visibility across hybrid environments.
- Support cloud repatriation analysis. Provide infrastructure capacity information that can help organizations evaluate whether moving AI workloads back on-premises makes sense.
Want to see how Sunbird's world-leading DCIM software can help you manage the on-premises side of your hybrid AI infrastructure? Get your free test drive now.