The adoption of Artificial Intelligence (AI) and machine learning (ML) models has become a cornerstone of modern applications, yet their implementation often incurs significant costs – particularly as applications scale to accommodate a growing user base. These costs are driven by the computationally intensive nature of these models and the expectation for rapid response times.

However, with the right deployment strategies and optimizations, it is possible to achieve substantial cost savings while ensuring applications scale efficiently and maintain high performance.

Our mission is to deliver innovative deployment solutions with a core focus on resource efficiency and cost-effectiveness. We empower organizations to significantly reduce the expenses associated with AI and ML operations, enabling them to deploy and scale these technologies sustainably and profitably.

In the sections below, we detail how we support businesses in achieving these outcomes, helping them maximize the return on their AI investments.

Multi-Model Deployment

A common deployment strategy involves dedicating a separate server to each required model. However, this approach often leads to underutilized or inefficiently used computing resources, resulting in high server costs. There are two main reasons why resources are wasted if only a single model is used per server:

Firstly, the server capacities for many models are too large to be used by a single model alone. For example, computer vision models usually range from 100 to 500 megabytes in size, while GPU servers typically start with 16GB of memory. This disparity often leads to servers being oversized, even when handling large batch sizes or multiple replications of the same model. Models are placed on a GPU to deliver fast response times. In many cases, the number of requests for a single model is insufficient to fully utilize the capacity of an entire GPU server. This leads to underutilized computing resources that still incur costs.

Secondly, modern applications often use several machine learning models, which are executed in a complex sequence and / or combined with each other. This is the case, for example, with model ensembles and model pipelines. If the models that are required in such a setting are deployed on different servers, it leads to a communication overhead. This leads to higher resource consumption and inference latency. It is most efficient to keep models that communicate with each other on the same server. Ideally, they should even share the same GPU, allowing intermediate results to remain directly in memory. Copying data to or from the GPU memory is often the bottleneck in modern AI-based applications, and should be avoided if possible.

Deploying multiple models on a single server comes with some challenges. For example, the correct server capacity must be selected so that the latency requirements can still be met. In addition, resource management (i.e. the allocation of storage space and computing capacity) is a complex issue.

We provide end-to-end deployment services designed to address every challenge along the deployment pipeline – no expert knowledge required. We support you at every step of the deployment process:

• Hardware Recommendations: Receive tailored suggestions for the hardware that best meets your performance and cost requirements.
• Custom Deployment: We deliver ready-to-use containers for on-premise deployment, optimized for your specific use case.
• Managed Hosting: Prefer to offload hosting? We can deploy and manage your models on our optimized servers, ensuring peak performance and efficiency.

Optimizing Models for Inference

Machine learning models are often taken from the software development department and deployed directly to the servers. However, this leaves a great deal of optimization potential untapped. This is because there is great potential for savings if the model is optimized for inference. For example, optimization can make the inference time of the model up to 36 times faster. Many model optimization techniques do not change the output values of the model and therefore do not lead to a loss of performance.

Below are a few examples of possible model optimization techniques:

• Layer Fusion: Combining multiple computational instructions into a single operation to reduce memory usage, improve runtime efficiency, and minimize latency during model inference.
• Execution graph optimization: Models are often defined by building blocks, which are executed in parallel or after each other (like for example layers in the case of neural networks). The execution of these building blocks can often be optimized for faster and more efficient computations.
• Hardware specific optimizations: Often, some computations can be made much more efficient by using hardware accelerators and instruction sets the right way. This needs to be done on a case-by-case basis after analysing the available hardware.

Keeping track of the vast array of optimization techniques available can be overwhelming. Determining which methods will deliver measurable improvements for a specific model is often even more challenging. Our deployment services eliminate this complexity by analyzing and optimizing your models for inference and deployment. We employ advanced techniques that preserve the accuracy and outputs of your models while significantly enhancing their speed and reducing resource consumption. With our approach, you can achieve optimized performance and cost-efficiency without compromising on model integrity or results.

Setting deployment parameters right

There is also great potential for optimization in the way models are deployed. We differentiate between deployment parameters and deployment tools. Deployment parameters determine, for example, what the maximum batch size should be for each model in order to use the server as efficiently as possible. Selecting the right deployment tools, on the other hand, relates primarily to the infrastructure on the server, such as the API that forwards requests to the models.

The most important deployment parameters are the maximum batch size and the number of concurrent executions of each model. Both parameters directly influence the memory requirements of the model, as well as the required inference time. Under-dimensioning can result in long waiting times before a request to the model can be executed. Overdimensioning, on the other hand, wastes valuable server resources, as this results in idling servers. Selecting the correct deployment parameters can be challenging, especially in the context of multiple models being deployed to the same server. That is, because parameters must be selected depending on the available resources and the expected number of requests for each model.

A whole stack of tools is required to deploy models to a server. These include, for example, the API, which receives the requests for the models, or the frameworks, which execute the models to answer the requests. We go into the required components in more detail on this page. As part of our service, we offer an end-to-end solution in which we have greatly optimized the deployment stack. We have focused on resource efficiency and speed, which can lead to significantly faster inference times compared to other solutions.

We approach deployment as a multidimensional optimization problem, where performance, cost, and resource efficiency must be balanced seamlessly. Our service provides advanced tools and methodologies to help you identify the optimal solution for your unique deployment needs. By that, you benefit from cutting-edge deployment strategies without dealing with the underlying complexities. With our service, you gain access to a streamlined and efficient solution for tackling even the most challenging deployment scenarios.

Get in Touch!

Partner with us to optimize your models and unlock their full potential. Share your requirements, and we’ll show you how we can drive your success.

Need more information? Reach out today to discuss how we can help you achieve your goals.