Companies that provide AI and machine learning technologies have traditionally maintained their own servers, either in a privately-owned data center or with on-premise servers. Today, we’re seeing some of these companies move their services to the cloud.
Artificial intelligence and machine learning processes require a significant amount of computing resources, so in a general sense moving to the cloud is logical. However, there are several factors preventing AI from reaching its full potential in the cloud – factors that technology will solve given enough time.
Cloud Computing Provides Virtually Unlimited Resources
Unlike an on-premise server limited by hardware components, cloud computing can provide access to virtually unlimited computing resources. Without cloud computing, the only way a company can get the massive resources required to run machine learning software is with a large budget, plenty of space, and the personnel to manage multiple servers.
It’s possible to get powerful servers and a dedicated IT team to manage them on-premise, but it’s not ideal under most circumstances. If you’re considering upgrading your company’s on-premise servers to meet your growing business needs, consider moving to the cloud as an alternative option.
If you’re running machine learning software, the speed and reliability of on-demand resources will make your software run smoother than ever. If, however, you’re running AI-based software, the cloud could slow you down.
AI Requires Lightning-Fast Speed
Cloud computing helps organizations meet their needs without having to operate their own datacenters or deal with servers on-premise. However, many AI processes are time-sensitive, and that’s where most cloud servers fall short.
Not all cloud computing falls short for AI software; it all depends on server components, software, and several other factors. In general, AI companies can’t just sign up for any cloud hosting account and expect things to work. For AI software to deliver its full potential, powerful processing is required.
Processing Accelerators Are The Future
To speed up the delivery of AI-powered software services, some companies are building processing accelerators. For instance, Groq’s tensor streaming processor (TSP) is one of only two commercially available accelerators. This processor is specifically designed to accelerate AI workloads in the cloud and is capable of 1,000 TOPS (1 Peta operations per second). According to Groq, these chips can achieve 21,700 inferences per second which more than doubles GPU performance.
Jonathan Ross, Groq’s CEO, told wccftech.com, “these real-world proof points, based on industry-standard benchmarks and not simulations or hardware emulation, confirm the measurable performance gains for machine learning and artificial intelligence applications made possible by Groq’s technologies.”
Can Processor Accelerators Reduce Or Eliminate Latency?
Latency is the biggest obstacle to cloud technology becoming standard for AI-powered software. When there’s nothing at stake, slow AI responses will have little to no consequence, aside from user frustration. For example, using AI to interpret the results from an MRI doesn’t require instant data transfer. However, when relying on AI for life-or-death situations (like safety), any delay can be deadly.
For instance, if a sensor in an autonomous car senses a pedestrian, engaging the brakes just one tenth of a second too late could have fatal consequences. In this kind of situation, AI software needs to be able to make decisions instantly. Having to relay information back and forth over a network won’t cut it.
Since AI processes require working on a stream of data in real-time, Groq’s TSP chip promises a 17x latency advantage over standard chips. If you’re interested in Groq’s TSP, you will need to become a Nimbix customer. Currently, the chip is only available for selected customers on the Nimbix cloud.
Demand For Speed Will Push The Next Cloud Breakthrough
Demand is on the rise for servers, especially for online casinos with darmowe gry hazardowe, that can support both machine learning and AI-powered software. As the cloud continues to become the gold standard in software deployment, technology companies have no choice but to continue improving processors and find new ways to close the gaps on latency.
Cloud computing is already powerful and dynamic. Resources can be spun up instantaneously on-demand and customers only pay for what they use. Once speed catches up to the power of cloud-based scalable resources, AI companies will finally be able to retire on-premise servers and move their services to the cloud.
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