NVIDIA Datacentre GPU Comparison | Buyers Guide

NVIDIA datacentre GPUs feature enterprise-class components for better reliability, ECC memory to protect against data corruption, certified drivers for professional applications, larger memory buffers, extended warranty support, and security features like disabling USB-C ports. They are designed for 24/7 operation under full load in server environments.

NVIDIA datacentre GPUs are available for purchase installed in a server or as virtual GPUs in Scan Cloud.

Workstation GPUs are actively-cooled cards designed for desktop PCs. In contrast, datacentre GPUs are passively-cooled cards designed for servers, often but not always with more onboard memory, enabling them to process larger models.

For AI training, the B300 and B200 are the top choices for large-scale deep learning and LLM training due to their exceptional Tensor core performance and high-bandwidth memory. The RTX PRO 6000 Blackwell Server and RTX PRO 4500 are excellent alternatives for organisations with graphics and AI requirements.

NVLink is NVIDIA's high-speed interconnect technology that allows multiple GPUs to communicate directly at speeds far exceeding PCIe. This is critical for multi-GPU training scenarios where data needs to be shared rapidly between GPUs. The H200 and H100 support NVLink at 900GB/sec, while older cards like the A100 support 600GB/sec.

NVIDIA datacentre GPUs must be purchased as part of a 3XS Systems server build rather than being available standalone. This ensures proper integration, cooling, power delivery and support for these high-performance components. Contact our team to discuss your server requirements.

For virtual PC (vPC) sessions running everyday office applications, the A16, L4 and A2 are optimised choices. For virtual server (vWS) sessions requiring more graphical performance, consider the L40S, L40, A40 or A10. The choice depends on the number of concurrent users and the graphical demands of their applications.

Multi-Instance GPU (MIG) enables multiple users to share access to a GPU. It fully isolates at the hardware level allowing memory, cache and cores to be partitioned into as many as seven independent instances.

The graphics card is the most important component in a server PC as it is responsible for rendering applications, performing simulations and running AI models. More powerful graphics cards enable higher resolutions and frame rates, improving user experience.

A graphics card comprises two main components: the GPU (Graphics Processing Unit) and VRAM (Video Random Access Memory), which stores models, textures and frame buffers.

A CUDA (Compute Unified Device Architecture) core is the primary processor inside NVIDIA GPUs. Thousands of CUDA cores work together in parallel; generally, more cores means higher performance.

Some workloads are accelerated by specialised RT or Tensor cores. CUDA core capability also improves with newer architectures such as Blackwell (2025), Ada Lovelace (2022) and Ampere (2020).

RT (Ray Tracing) cores accelerate real‑time ray tracing, simulating realistic light, reflections and shadows. More RT cores generally result in higher frame rates when ray tracing is enabled.

RT core capability increases with each generation (e.g. 5th gen vs 4th gen).

Tensor cores accelerate AI and machine‑learning workloads such as training and inference. GPUs with more advanced Tensor cores deliver significantly higher AI performance.

Each GPU generation brings more capable Tensor cores with support for newer data formats.

DLSS (Deep Learning Super Sampling) is an AI‑based rendering technology that uses Tensor cores to increase frame rates while maintaining image quality, allowing higher resolutions without the usual performance cost.

NVLink is a high‑bandwidth interconnect that links compatible NVIDIA GPUs together, allowing them to share memory and data directly for improved multi‑GPU performance.

A FLOP (Floating‑Point Operation per Second) measures how quickly a GPU performs complex mathematical calculations used in simulations and AI workloads.

FLOPS are commonly expressed as GFLOPS (10⁹), TFLOPS (10¹²) or PFLOPS (10¹⁵), similar to KB, MB, GB and TB for memory capacity.

These values describe floating‑point precision. Lower precision formats such as FP4 and FP8 are faster, while higher precision formats such as FP32 and FP64 provide greater numerical accuracy at lower performance.

A TOP (Trillion Operations per Second) measures how quickly a GPU can perform integer operations, commonly used when inferencing AI models.