The Canadian AI development market is thriving. The Government of Canada announced a C$2.4 billion investment to support the Canadian AI sector as part of the federal budget just recently. With C$2 billion of this investment being allocated towards AI infrastructure and business development initiatives as part of the AI Compute Access Fund facilitating AI research, startup scaling, and providing other support for AI organizations—the future of AI research and innovation in Canada looks bright.

In this exciting landscape, you are probably looking for options to expand and elevate your development teams—and a high-performance workstation built specifically for AI is one of the best ways of facilitating this.

Why do you need a powerful workstation for AI?

Training complex AI models is no small feat, it involves processing massive amounts of data and performing intricate calculations—tasks that regular workstations simply do not possess the ability to handle. This is why a powerful workstation is non-negotiable for AI companies.

A high-performance workstation equipped with the right hardware can significantly reduce the time it takes to train models. This enables faster iterations and enables businesses to explore new possibilities of AI—driving unprecedented innovation and growth.

How to build a powerful workstation for AI development

A powerful workstation naturally comprises powerful hardware, however, there are some considerations that you need to make when choosing specific components. Certain manufacturers have specific hardware tailored for AI development as well as identifying and understanding the need for these components will go a long way in helping you build the ideal workstation for AI. You can also take some additional steps to ensure a smooth and efficient AI development experience. Let's dive in.

The CPU

While the CPU is not the most important component of an AI workstation, it is still a key part of your build. It handles various tasks during AI development, such as pre-processing data, managing computations, and executing core functionalities. While some algorithms do not extensively leverage parallel processing, a powerful CPU will make the development process much smoother.

Here are some key factors to consider when choosing the CPU:

  • Core count

    More cores mean the ability to handle multiple tasks simultaneously, and for certain AI development processes, this might be a requirement. Aim for a CPU with a high core count – think 16 or more – for optimal performance.
  • Clock speed

    Higher clock speeds mean faster processing of individual tasks. While this is an important consideration, for AI development core count tends to matter more than clock speeds. Go for a CPU with a good balance between core count and clock speed for the best results.
  • Manufacturer options

    When it comes to CPUs for AI, both Intel and AMD have very compelling options. We tend to opt for AMD, as you can't go wrong with the Threadripper PRO 7000WX-Series—offering up to 96 high-performance cores.

In addition to this, if you're tackling exceptionally complex AI models, consider a dual-socket motherboard that allows the installation of two CPUs. While this is a very niche option to go for, it significantly boosts processing power at the expense of increased costs and complexity.

The GPU

This is the star when it comes to AI development. According to Microsoft, GPUs significantly outperform CPUs when it comes to the inference of deep learning models. Their testing showed that a 35-pod CPU cluster was consistently outperformed by a single GPU cluster by at least 186% and by 415% with a 3-node GPU cluster, for similar costs.

This is arguably the most important component of an AI workstation. Here's what you should consider when choosing one:

  • VRAM

    Since VRAM stores the data that the GPU actively works on, aiming for a GPU with at least 16 gigs or more is ideal for handling large datasets.
  • Processing cores

    Here, too, different manufacturers offer different options. When it comes to NVIDIA GPUs specifically, CUDA cores and Tensor cores should be at the forefront of your consideration. CUDA cores are ideal for general-purpose computations, while Tensor cores are specifically designed for deep learning tasks. For AI development, a GPU with a high number of Tensor cores is ideal.

Here are some recommendations based on the different levels of performance you might need for AI.

  • High-performance tasks: For demanding tasks requiring the absolute best performance, the NVIDIA A100 is the current king.
  • Medium-scale tasks: The RTX A6000 offers a compelling balance between performance and cost for medium-scale AI projects.
  • Smaller-scale tasks: For projects with smaller datasets and less demanding workloads, the consumer-grade RTX 4090 is a viable option.

If you are considering a multi-GPU setup, there are some trade-offs you need to take into account. While distributing workloads across multiple GPUs can significantly reduce training times, it also increases the cost and complexity of your system. It's best to carefully evaluate your specific needs and budget before deciding on which configuration to go for.

RAM

RAM is crucial for smooth data storage, instruction retrieval, parallel processing, and GPU acceleration for AI development processes. Here's what you should consider when choosing RAM for an AI workstation.

  • Capacity

    16 gigs of RAM is a good starting point for AI development. However, 32 gigs of RAM is recommended for large datasets or complex models.
  • Type

    DDR4 RAM is ideal for the latest systems, however, if the budget allows, DDR5 will offer the absolute best performance.

Storage

While SSDs are the norm for any configuration today, it's best to complement the storage configuration with an HDD for AI development as it tends to require high storage capacity for storing datasets.

When it comes to SSDs, NVMe SSDs offer the best performance, and Samsung's PM9A3 NVMe PCIe® 4.0 SSD – available in 960GB, 1.92TB, 3.84TB and 7.68TB models – is a great option. It reaches sequential read/write speeds of 6,900/4,100 MB/s and random read/write speeds of 1.1 million/200,000 IOPS. This, along with implementing Samsung's disaggregated storage architecture, can dramatically improve the performance of your AI workstation.

The ideal storage configuration would be a powerful SSD that stores training data and operating systems, and a high-capacity HDD that stores large datasets and project archives.

Additional considerations

In addition to these, here are some additional considerations you need to make for the ideal AI workstation.

  • Cooling System

    Powerful hardware generates significant heat. Invest in a robust cooling system with efficient fans and proper airflow management to prevent overheating and ensure optimal performance.
  • PSU

    Don't underestimate the importance of a reliable PSU. Choose a high-wattage PSU (ideally 850W or more) to provide sufficient power for your demanding hardware components. It's also best to choose PSUs with 80 Plus Gold or Titanium certifications to ensure power efficiency.
  • Software compatibility

    Ensure your chosen hardware components are compatible with popular deep learning frameworks like TensorFlow and PyTorch. You can check the documentation of these frameworks for recommended hardware configurations.

Build a high-performance workstation for AI development for improved workflows

By carefully considering the factors mentioned above, you can build a powerful workstation tailored to your specific AI development needs.

Opting for a custom business PC built by experts is ideal in this case, delivering a workstation for AI that can significantly accelerate your training times, improve workflow efficiency, and unlock new possibilities in your AI endeavors.