AI Development Hardware Buyers Guide

AI Model Development

When it comes to using your prepared data in model development, chances are your project isn't unique, so there may well be pre-trained frameworks or optimised foundation models (FMs). These save time and effort when building an AI pipeline, and removing the need for much of the subsequent development work. A prime example is NVIDIA AI Enterprise (NVAIE), which features in excess of 30 distinct and interlinked pre-trained frameworks, optimised for NVIDIA GPUs, designed for end-to-end implementation of AI projects such as medical imaging, autonomous vehicles, avatars, drug discovery, robotics, generative AI and many more.

Understanding the frameworks or FMs available to you may also have a significant impact on your AI development platform choice, as it may dictate a minimum GPU memory size or level of performance required to complete this phase within a given timeframe. When it comes to model size, some FMs may already number into the several billion-parameter space, before you start fine-tuning them into your own tailored (often even larger) model. It is therefore key to understand the relative size of models, how they might scale and the GPU hardware that will be capable of handling this. Just for context, between the 1950s and to 2018, AI model size grew by seven orders of magnitude (from 000's to 30M) – yet from 2018 to 2022 alone, it has grown another four orders of magnitude (from 30M to 20B). Today 400B parameter models are not uncommon and ChatGPT-4 is rumoured to have 1.8T parameters.

It is worth clarifying that this table is for guidance only - absolute size (in GB) of any model is determined by number of parameters and the size of each parameter. Similarly, there will be small agentic AI models if their function is very focused and there will be very large LLMs if translation of many languages is the goal. It is also worth pointing out that the dataset size mentioned is the likely final size, so you need to consider capacity for numerous versions and many many iterations before reaching the final model. You can find more in-depth information about model development by reading our A-Z of AI White Paper.

Comparative Performance

When choosing which AI development platform is right for your needs its important to not only look at its raw compute performance (typically measured in TOPS or FLOPS) but also the amount of memory, as this determines how large your AI model can be (typically measured in billions of parameters). This is quite a fundamental shift from a few years ago, when raw compute performance was the most important factor, and is due to the increasing use of LLMs when it comes to developing AI models. The table below compares the performance of the most popular systems.

* combined figure for all GPUs

Which is the best AI workstation?

Thanks to its innovative SoC architecture, the DGX Spark is the only reasonably-priced system that can develop AI models with a large number of parameters, such as generative AI and reasoning AI models. The DGX Spark is not only cost-effective up-front, but has a very low TCO thanks to its frugal power consumption. Being a desktop device, the DGX Spark is also a practical choice for organisations that for policy reasons are unable to upload their data into a cloud service.

3XS AI Workstations are a supremely capable AI development solution. They are available in a wide variety of configurations from just one GPU, to as many as seven GPUs, enabling you to match the spec to your project and budget requirements. This is the best solution if you need to gradually scale, just bear in mind that this comes at the cost of comparatively high TCO, as using multiple GPUs is less power efficient than a single all-powerful SoC.

Like the DGX Spark, the DGX Station GB300 also has an innovative SoC architecture, enabling it to develop AI models at enormous scale and great speed. The DGX Station is not only the best AI workstation for single-users, but also small teams, with up to seven users being able to share access simultaneously.

Finally, a 3XS AI Development Laptop is the only solution that enables you to develop AI models from anywhere - albeit with some quite severe memory limitations. It's therefore advisable to pair a laptop alongside a desktop device or cloud service, so that work needn't stop when you are on the move.

AI Development in the Cloud

Although this guide has been concerned with AI development hardware, we also supply all of these GPU-accelerated systems as virtual systems on our Scan Cloud platform.

Simple, Flexible Pricing with No Hidden Fees

No long-term commitments, no extra charges for storage or networking—experience the full benefits of the cloud without the drawbacks.

Reference Architecture for Unrivaled Performance

Harness the power of the latest GPUs for desktops or high-performance GPU clusters, from single GPUs to 8-way systems.

Networking and Storage Built for Performance

All GPU instances include NVMe storage and uncontended network ports, ensuring top-tier performance and data privacy.

Build It Your Way

Custom builds are SCAN's specialty—every aspect of our Infrastructure-as-a-Service (IaaS) solutions is fully customizable, with UK-based solutions ensuring data sovereignty.

Browse the available Scan Cloud options on or contact our Cloud team.