Over the last 18 months, AI has exploded into our everyday lives. It’s on our phones, it’s embedded in our search engines, social media, navigation systems, and even our healthcare and financial services. The rise has been meteoric, and it’s showing no signs of slowing down.

But here’s where it gets even more interesting: you don’t have to be a passive consumer in this AI revolution. You can create your own AI server, and start developing your own AI applications, breaking free from the constraints of the big AI providers.

AI servers are a popular solution in the field of artificial intelligence (AI); AI servers are used to execute complex AI workloads, including training and inference of sophisticated AI models. This article will introduce you to the core concepts of AI servers, their architecture, and functionality.

Understanding AI Servers

An AI server is a powerful computing system purpose-built to handle the computational demands of artificial intelligence tasks. Unlike traditional servers designed for general-purpose computing, AI servers boast specialized hardware and software components optimized for AI computations.

While the hardware requirements for AI have historically been substantial, recent advancements have significantly lowered the barriers to entry, making AI capabilities accessible to a far broader audience.

Let’s dive into what is needed for an AI server:

GPU Horsepower

Graphics cards are not just for gaming; they are proving hugely successful in powering AI. Specialized graphics processing units (GPUs) such as NVIDIA Tensor Core GPUs or AMD Instinct GPUs speed up AI processing capabilities substantially. A GPU accelerates the complex mathematical operations that form the foundation of AI and deep learning. This translates to faster training of AI models and quicker real-time inference, enabling advancements in various fields like natural language processing, computer vision, and more.

High-Performance Processors

AI models work best on servers with good processing power. A high clock speed and a large number of processing cores benefit performance greatly. Consumer-grade AI servers typically use high-performance central processing units (CPUs) like the Intel Xeon Scalable processor or AMD EPYC processors. While a powerful GPU is usually the star of the show in AI acceleration, a strong CPU acts as a vital supporting member that ensures the entire AI workload runs smoothly and efficiently.

Expansive High-Speed Memory

AI models and datasets can be enormous. Consequently, AI servers typically possess substantial amounts of high-bandwidth memory, often in the various form factors of DDR5/6 or HBM (High Bandwidth Memory). The more memory available to the AI workload, the larger AI model can be used, resulting in more detailed and typically more accurate results.

Fast SSD and NVMe Storage

AI training often needs rapid access to data and frequently utilizes high-performance storage like NVMe (Non-Volatile Memory Express) solid-state drives (SSDs) to enable efficient data movement. A fast disk file system is essential to keep up with the CPU and GPU performance and to avoid the risk of having any potential bottlenecks.

High-Speed Networking:

In distributed AI environments, seamless communication between the AI server stack is critical. AI servers typically feature high-speed networking capabilities such as InfiniBand or 2.5, 5, or 10 Gigabit Ethernet to ensure efficient data transfer between the nodes and the storage layer system.

The Role of AI Servers in Generative AI

Generative AI is a subfield of AI that focuses on creating content like images, text, and music and has experienced remarkable growth in recent years. 18 months ago, few knew of the three big players in AI: ChatGPT, Gemini, and CoPilot, each of which have now become household names. Businesses are investing huge resources in AI and machine learning, with many banking on the success of AI to give their business a competitive advantage.

This growth has been fueled by the advancement of AI servers capable of handling the computational demands of training and deploying large language models (LLMs) and other generative AI models. Generative AI training often involves processing large datasets and executing complex algorithms, making the capabilities of AI servers pivotal to their success.

Traditionally, users would pay a subscription to an LLM provider like ChatGPT, and then consume the service directly on their platform using prompts and API integration. This is already starting to change as businesses and users get smart to the idea that it’s relatively easy to do it yourself.

The open-source community has been pushing free-to-use LLMs for the consumer market, and big corporations like Meta (Facebook) are investing billions of dollars in their development. Some popular open-source models include Llama, StableLM, Dolly, Bloom, and OpenAssistant.

AI Workloads and Data Centers

It’s now much easier to run AI computations in the data center. Atlantic.Net is fully committed to providing AI-ready services to our customers from all of our data center locations. You can create an AI-ready VPS in a few clicks from our Atlantic.Net control panel in any of our data centers across our VPS regions. We have locations in the United States, Canada, Europe and Asia.

Atlantic.net offers a range of VPS options that can effectively handle AI-driven processes. We have recently partnered with the Nvidia Corporation to offer optional GPU configurations (region-specific) for unparalleled performance.

Enhance your experience by pairing this new service with our extensive VPS catalog, offering plans tailored for everything from general use to specialized storage, memory, or compute needs.

Here is a summary of the type of plans we have available.

General Purpose & Compute Optimized:

Our general purpose and compute-optimized VPS instances provide a good starting point for various AI-driven processes. The higher-tier options within these categories, like G3.96GB, G3.128GB, C2.32GB, and C2.64GB, offer substantial RAM and support the vCPU counts that are crucial for handling the computational demands of AI.

Integrating GPUs with these instances can supercharge their AI capabilities, particularly for tasks like deep learning and complex model training. Combining the latest gen Intel Xeon scalable CPUs from Intel Corporation, ample RAM, and a powerful GPU creates a highly performant environment for tackling demanding AI projects.

Dedicated Hosts:

Dedicated servers deliver unparalleled performance for resource-intensive AI tasks with their dedicated resources and isolation. When coupled with high-end NVIDIA GPUs like the H100 NVL or H200, these servers become powerhouses capable of easily handling the most demanding AI models running at scale.

GPU-Specific Considerations:

Choose the GPU model based on your AI tasks. The H100 NVL excels at large-scale AI models, while the L40S balances performance and cost-effectiveness for a broader range of AI applications. Engage with our sales team to discuss your AI workload requirements and identify the most suitable GPU configuration.

These configurations, combining powerful CPUs, ample RAM, and cutting-edge GPUs, unlock the full potential of AI, enabling faster training, more complex model handling, and superior performance overall.

Tips for Choosing and Deploying AI Servers

Are you ready to start your AI server journey? Do you need a server that can handle diverse AI applications in operation, from training large language models to running inference workloads for various industries?

Here are some of our top tips to get you started.

Assess Your AI Workload Requirements

Identify Your AI Tasks:

Pinpoint the specific AI applications you’ll be running, such as deep learning, natural language processing, computer vision, or generative AI. Each task has different computational and memory requirements, pick an AI application and understand the recommended system requirements for the task at hand, this will help you identify the right hardware and software mix to achieve AI success.

Different AI solutions have distinct system requirements. Deep learning relies heavily on GPUs for matrix operations, while Natural Language Processing (NLP) works best on powerful CPUs for text processing. Computer vision requires high-performance GPUs for image/video analysis, while generative AI needs both strong GPUs and CPUs for handling large models and generating complex outputs. All AI tasks benefit from ample high-speed memory and fast storage.

Estimate and Test Resource Needs:

Evaluate the size of your datasets, the complexity of your AI models, and the expected inference and AI training workload. Consider the scale of your ambitions – are you dealing with large models or aiming for large-scale deployments? This will help you determine the necessary GPU horsepower (potentially leveraging technologies like NVIDIA NVLink for multi-GPU setups), the number of gen Intel Xeon Scalable processors or AMD EPYC cores needed, the optimal GPU memory capacity, and the storage performance required to keep your data flowing smoothly.

Choose the Right Deployment Model

Remember, you can host your AI deployment pretty much anywhere. If you have the capital, it’s possible to purchase or lease your own server. However, the costs can be very high for the top-of-the-line systems, which is why many users opt for a pay-as-you-go cloud model or a 1 to 3 year fixed term contracts.

On-Premises:

For organizations with compliance data control requirements or those in sectors like scientific research where data sovereignty is necessary, on-premises deployment might be the preferred choice. High-Performance Computing (HPC) workloads, which demand high-end computing power, often fall into this category. Remember, on-premises solutions require significant upfront investment and ongoing maintenance. Consider factors like space, power consumption, and cooling solutions – liquid cooling might be necessary for high-density deployments.

Cloud:

Cloud-based AI servers offer flexibility, scalability, and full support, plus you get pay-as-you-go pricing. They can be an excellent choice for handling fluctuating workloads and avoiding upfront infrastructure costs, you can also dip in and out when you need it.

Hybrid:

A hybrid approach, where some cognitive computing jobs run on-premises and others in the cloud, combines the benefits of both models, allowing you to leverage their strengths based on your specific business needs, and when needed offload AI computing power to the cloud.

Optimize AI Server Performance

Software Optimization:

There are lots of paid and open-source AI-specific software frameworks and libraries available. To streamline development and maximize performance, look for mature and optimized code sets and software that works well within specific resource allocations. Also, look into vector database hosting for backend optimizations.

Hardware Acceleration:

We have already discussed the importance of GPU acceleration in AI Inference. Picking a powerful VPS host is also essential.

Load Balancing:

Use application load balancers to distribute AI workloads across multiple servers and ensure optimal resource utilization. Advanced AI solutions can require a cluster of nodes to ensure peak performance and avoid performance bottlenecks.

Monitoring and Management:

Take the time to implement monitoring and management tools to track the AI server performance. The overhead on these tools is now quite affordable, but the insights you can gain to improve performance and optimize your applications can be a lifesaver when helping to identify potential issues, and proactively address them.

Plan for Scalability

Choose a Scalable Architecture:

Design your AI infrastructure with scalability in mind, allowing you to add more servers or GPUs as your AI workloads grow, ensuring your AI solutions can keep pace with the evolving demands of your business. This can involve horizontal and vertical scaling, using clustered nodes and application load balancing.

Leverage Cloud Elasticity:

If using cloud-based AI servers, take advantage of the elasticity of the cloud to scale resources up as needed, providing the agility to respond to changing AI workload demands efficiently.

Atlantic.Net AI Server Hosting

Our commitment to AI excellence is evident in our partnerships with industry leaders like NVIDIA and our continuous investment in AI-ready infrastructure. Our range of VPS options, dedicated hosts, and GPU configurations ensures that you have the flexibility to tailor your AI environment to your specific workload requirements.

Why Choose Atlantic.Net for AI Server Hosting?

  • Unparalleled Performance: The latest generation Intel Xeon Scalable processors, high-bandwidth memory, fast NVMe storage, and cutting-edge NVIDIA GPUs to achieve optimal AI performance.
  • Expert Support: Our dedicated team of experts is available to guide you through every step of your AI journey, from choosing the right configuration to optimizing your deployment for maximum efficiency.
  • Cost-Effectiveness: Our transparent pricing models ensure that you only pay for the resources you need, making AI server hosting accessible to businesses of all sizes.

Take the Next Step in Your AI Journey

Don’t let infrastructure limitations hold back your AI ambitions. Contact Atlantic.Net today to discuss your AI server hosting needs and discover how we can help you unlock the full potential of AI.