The largest technology providers – including AWS, Google and IBM – are all embracing consumption-based GPU services, giving users access to what is usually an expensive and unwarranted hardware investment. Instead of paying for a large GPU hardware infrastructure upfront when high-powered projects come along, they can essentially ‘rent’ the compute they need, when they need it.
This concept is being taken to the next level with the introduction of GPU cloud services, hosting both the software and the hardware functions of data analytics as a complete SaaS platform. This means all the necessary resources needed for businesses to perform advanced analytics is made accessible through one source.
Businesses are therefore saved from having to buy, upload and manage their software separately to their ‘rented’ hardware. Plus, it means businesses can turn their hardware on and off without fear of losing their data, resulting in significant savings by reducing hardware run time and costs.
The transition opens the door for more businesses to harness the power of analytics, and the same concept can be applied to GPUs. Our upcoming cloud solution makes GPU acceleration accessible online – making it even easier to access all the necessary resources required to complete high-intensity workloads and advanced machine learning techniques on a pay-as-you-go basis.
Training, AI and Machine learning
High performance computing is absolutely necessary for businesses to harness advanced analytics capabilities, such as machine learning or exploratory investigations.
AI and machine learning models require in-depth training based on vast datasets to be able to function, accurately. The training process therefore necessitates performing complex calculations and processing large real-world datasets. Organisations can also perform back-testing to assess the accuracy of the model against historical data.
In these instances, businesses need GPU hardware to accelerate their resources and crunch large amounts of data extremely fast. However, once training is complete their GPU resources may be left idle for long periods of time.
Similarly, analysts performing investigations require the processing capabilities of GPUs, but are only accessing this power while at their desks. Again, this leaves resources idle overnight and potentially entirely unused between research projects. Many businesses are therefore only performing basic numeric workloads and data crunching functions, not high-performance workloads, on a day-to-day basis.
Stop oversizing your hardware
Resultantly, for many organisations, buying GPU hardware upfront in order to support ad hoc AI and machine learning training is an unjustifiable expense, and, ultimately, they are forced to abandon these projects altogether. Alternatively, organisations can size their hardware for their biggest, but most infrequent, workloads. This means they end up investing in large GPU resources which, most of the time, lie idle.
Cloud solutions offer an affordable solution to this challenge.
Cloud GPUs will have large compute ready for ad hoc workloads, enabling organisations to plug in to the power they need and seamlessly disconnect when the workloads are complete. This way, they’re only paying for what they’re using, while they’re using it.