Deploying a trained AI / ML model is often a challenge. In particular, the question of which hardware is required for a given use case is often a puzzle. On the one hand, it must be ensured that the responses of the model are given fast enough, taking into account the expected request volume. On the other hand, the operation of servers is very expensive and it should therefore be avoided that server resources remain unused.
There is a wide range of alternatives for deploying models. For example, you have to decide whether you need a server with or without a hardware accelerator such as a GPU. Other typical design criteria are the number of CPU cores, the RAM size and the hard disk size. Choosing the right hardware is particularly difficult if you deploy multiple models to a single server, which interact differently with the hardware.
It is often proposed to provide and scale resources only on request. However, this leads to cold start problems, i.e. waiting times until the instance is provisioned. In many applications, the provision of resources on request is therefore not possible. Instead, the minimum requirement for computing resources that must be kept available should be determined. In this way, the models are kept available and can respond quickly, while the use of resources is kept as low as possible.
Servers with GPU accelerators are usually much more expensive compared to CPU-only based servers. CPU servers are often underestimated, as they are sometimes sufficient to fulfill the request requirements. GPU servers, on the other hand, are typically a good choice when multiple models are deployed, as these can share the computing resources. The key to a low-resource deployment therefore lies in knowing exactly which computing resources are needed in the corresponding case.
How to find the right hardware?
The best way to find the right hardware is to deploy the models to different servers for testing and measure the achieved performance. As there is a wide range of different hardware constellations, this involves analyzing a large number of different servers. The deployment configuration should be optimized for all these servers so that the results achieved are comparable in a fair way.
Another difficulty in finding the right architecture is that several instances of the server are usually operated in parallel in order to be able to answer all requests quickly enough. Therefore, when searching for the right server architecture, it should also be compared whether several inexpensive servers with a weak architecture (operated in parallel) deliver better results than an expensive server with a strong architecture. This leads to further server combinations that need to be tested during the search.
Manually searching for the best server architecture for your machine learning models can be time-consuming and inefficient. That’s why we offer a powerful benchmarking service to streamline this process. We optimize the deployment configuration for your model on each target server to ensure peak performance. The model is then benchmarked on the target hardware to generate detailed performance metrics.
With these insights, you can precisely determine the ideal hardware for your specific use case, achieving the perfect balance between performance and cost. In many cases, this analysis shows that a combination of cost-effective servers can fulfill your requirements, helping you significantly reduce server expenses without compromising on response times.
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