Managing Inference Workloads in the Cloud

Cloud-based inference workloads present a unique set of challenges. Here's what you need to know to optimize your cloud resource usage and meet SLAs while keeping costs under control.

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The Cost of Online Inference

The real-time nature of online inference applications puts high demands on your cloud resources and can really drive up costs. End users can't wait 30 seconds for their self-driving car to avoid a crossing pedestrian, but unfortunately, most of us can't throw unlimited compute resources at the problem to guarantee low latency and fast response times.

The goal of running inference at scale is to maintain performance cost-effectively while meeting the needs of the end user.

Focus on the Key Challenges

GPUs are much faster than CPUs (even for inference workloads), but they require effective resource management or you won't see cost-efficiency.

Unfortunately, many organizations struggle to master their GPU scheduling and fall back to using CPUs for inference, which limits their productivity and growth potential. This guide breaks down four key dimensions of scaling inference workloads cost-effectively, to make scaling achievable without impacting performance.

“Rapid AI development is what this is all about for us. What Run:AI helps us do is to move from a company doing pure research, to a company with results in production.”

Siddharth Sharma, Sr. Research Engineer, Wayve

Managing Inference Workloads in the Cloud

Inference models are becoming a core pillar of cloud native applications. We discuss ways to operationalize these workloads in the cloud, edge and on-prem.

How to stay in control and maintain visibility when faced with inference workload sprawl

Fleet and lifecycle management at scale: multi-cloud deployments and efficient cloud resource usage

GPU fractions and descheduling to CPU to meet SLAs while keeping cost under control

The AI Cloud Platform of the Future

Run:AI's Atlas AI Cloud Platform manages everything from huge distributed computing workloads to smaller inference jobs.

Applications: Develop and run your AI Applications on accelerated infrastructure using the tools you want.

Control Plane: Gain centralized visibility and control across multiple clusters no matter where they are located.

Operating System: Schedule and manage any AI workloads - build, train, inference - via our cloud-native operating system.

Infrastructure Resources: Orchestrate AI workloads across compute resources whether they are on-premises or in the cloud.

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Run:AI, 2022