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Five Things to Consider When Deciding Where to Run Your AI Workloads
As the world becomes increasingly reliant on artificial intelligence (AI), it’s essential to consider the various factors that come into play when deciding where to run your AI workloads. With the rise of edge computing and cloud-based solutions, organizations are faced with a crucial decision: should they rely solely on cloud-based processing or adopt a hybrid approach by bringing AI inferencing closer to devices?
To make an informed choice, consider these five key factors:
1. Balancing Latency and Compute Requirements
Running AI models locally on the device not only reduces dependency on cloud resources but also significantly reduces latency. This is particularly crucial when it comes to real-time processing or situations where a swift response is necessary. On-device computing can even function without an internet connection, which becomes vital for tasks like endpoint protection AI agents that require constant monitoring.
On the other hand, for compute-intensive larger models at scale, cloud-based AI may be more suitable due to its unparalleled scalability and resource availability. A balanced approach that combines both on-device and cloud-based processing could provide a robust solution for organizations seeking optimal performance.
2. Security and Privacy Concerns
Cybersecurity and data privacy are the top concerns hindering GenAI adoption, as highlighted in the 2024 IDC CIO report. Organizations must be prepared to protect sensitive data such as personal identifiable information (PII) and company proprietary data. When deciding where to run your AI workloads, it’s essential to consider the type of data being processed, its intended use, and the potential risks involved.
For instance, if a software developer is working on a company’s proprietary code and requires an AI-powered debugging tool, running this on the cloud could be risky due to potential data breaches. Instead, bring the AI processing closer to the edge in a secure environment, providing better control over sensitive information.
3. Cost Savings Opportunities
As GenAI models evolve from testing phases to monetization, subscription-based services are emerging. However, it’s uncertain if this pricing model will scale efficiently across millions of queries and users. On-device processing can be an effective way to reduce costs by offloading inferencing tasks from the cloud. This approach will ultimately benefit end-users, who would otherwise bear the cost burden.
4. Sustainability Imperatives
AI deployments come with significant environmental implications, as highlighted by the International Energy Agency’s findings that a single ChatGPT request consumes 10 times more electricity than a Google search. The ever-increasing number of data centers operating GenAI also poses an issue.
To mitigate these effects, organizations must explore ways to make AI workloads more energy-efficient. A crucial aspect of this is comparing the power consumption of devices running AI on NPUs versus high-performance CPUs and GPUs in cloud environments. It’s imperative for tech companies to prioritize environmentally conscious AI deployment methods.
5. Hybrid Approach: The Future of AI
In conclusion, it’s undeniable that not all AI workloads can be brought to the edge. However, recognizing the potential of high-performance AI devices or workstations to run time-sensitive tasks locally in an efficient and secure manner is crucial. I envision a future where organizations seamlessly integrate cloud-based processing with on-device inferencing for optimal results.
A perfect example of this hybrid approach is seen in financial institutions using on-device AI to monitor transactions in real-time, flagging suspicious activities instantly. This could be supplemented by the cloud component handling complex and resource-intensive tasks, such as training fraud detection models from large datasets.
Source: http://www.forbes.com
