What is a serverless GPU?
If you want to make use of GPUs in your day-to-day operations, you typically have two options: Using an on-premise GPU or a serverless GPU (which is a cloud computing model). This article will focus on the latter of the two.
Serverless GPUs are GPUs that are located in a cloud environment. As such you don’t have to worry about underlying infrastructure (i.e. servers). Serverless GPUs are still physically located in servers, but the maintenance and provisioning of these servers is taken care of by the serverless GPU provider.
Most cloud providers require you to reserve compute resources for serverless GPUs. However, it can be difficult to predict compute usage over time. As such, you may reserve more resources than you end up using, incurring needless costs, or you may reserve less than you need and create a processing bottleneck. Alternatively, UbiOps only charges you for the compute resources you actually use, meaning you only pay for what you need.
Serverless GPU inference
Using a GPU for model training instead of a CPU can decrease training times by as much as 300% for deep learning models. This is because deep learning models require millions of calculations, which GPUs can perform simultaneously. This is because while CPUs are built with multiple cores, GPUs are built with thousands of cores, granting them superior parallel processing capabilities (learn more by reading our GPU guide).
Model inference requires less resources than training, which is why some people still prefer to use CPUs for inference. There are use cases, however, that require a faster inference speed or a higher accuracy for models to be effective. Image processing models, like those used in self-driving cars for example, need to be able to quickly process data to avoid accidents.
Machine learning models in healthcare can quickly become too complex for a CPU to process effectively, reducing the accuracy of these models. Use cases like these benefit massively from using GPUs, not only for training but for model inference as well.
If you want to make use of GPUs for model inference you not only need to acquire the GPUs themselves, but also the required infrastructure. The cost of a high-end GPU server can quickly run up to 50,000 USD. For a start-up, this scale of investment can be unattainable.
Luckily, today’s data science practitioners can access serverless GPUs on-demand. With GPUs on-demand, consumers can rent or use GPUs owned by cloud providers like AWS, GCP, or Azure. Making use of these serverless GPUs can save both start-ups and enterprise clientele the massive investment that is building their own GPU server. Maintenance of servers is also handled by the cloud provider, saving businesses additional money and manpower. With most cloud providers you do not have to pay for using a GPU if you are not using it, making it a cost effective option. When you do want to use a GPU however you need to reserve a timeslot for it, which could result in extra costs when the task you want to execute on a GPU takes less time than you initially estimated.
Optimizing for GPU usage
Serverless GPU solutions offer potential benefits for organizations looking to leverage GPUs for AI workloads. The on-demand, pay-as-you-go model offered by providers (such as UbiOps) can reduce the need for capital investment in GPU servers that may be underutilized. It also provides flexibility to access additional GPU power instantly when workloads require it.
However, accurately estimating resource needs is still important to minimize costs. Serverless GPUs may not be the optimal solution for every organization – for example, those with consistent, predictable GPU usage may benefit more from owning and operating their own GPU servers. Overall, serverless GPU offerings like those from UbiOps represent an emerging model that can expand access to GPU power for AI, especially for smaller organizations, but careful assessment of usage patterns and costs is needed to determine if it is the right fit.
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