Nvidia Graphics Cards

IT teams that need shared acceleration across mixed AI, analytics and visual workloads use Nvidia Graphics Cards to keep one GPU estate serving more than one job. The range supports a broad spread of data-centre demands, from mature mixed-workload platforms to higher-throughput AI and graphics environments, without forcing separate infrastructure for every use case.

In shared clusters and distributed server estates, they help maintain utilisation, control contention and keep work moving across inference, rendering, simulation and model development. That makes it easier to scale capacity, standardise procurement and avoid fragmenting management as workload demands change.

Nvidia Graphics Card Quick Specs & Key Features

Shared workload partitioning: Multi-Instance GPU on A100 splits one accelerator into isolated slices for separate jobs, allowing shared AI, analytics and HPC environments to keep utilisation high with lower operational overhead.
Transformer-class throughput: H100 uses Transformer Engine acceleration for large language model training and inference, giving mainstream generative-AI data centres faster model execution and better workload fit.
Memory-dense acceleration: H200 pairs Hopper compute with larger HBM3E capacity and bandwidth, letting memory-bound models and scientific datasets stay on-GPU for reduced scaling pressure and improved throughput.

Blackwell-era efficiency: B200 is the Blackwell Tensor Core GPU for frontier training and large-scale inference, helping organisations refresh into current-generation AI infrastructure for higher throughput per cycle.
Low-profile inference: L4 is a power-efficient Ada GPU for video, inference and edge analytics, fitting dense servers where lower power draw and cooling demands support easier scaling.
Visual-compute flexibility: L40S combines AI, rendering and media acceleration in one data-centre GPU, giving mixed creative and generative workflows a single platform with simpler deployment.

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Full technical specifications are available on each product page.

Model	Popularity	Deployment	Primary Use Case	Form Factor	AI Workload Type	GPU Architecture	PCIe Generation	Memory Bandwidth (TB/s)	Tensor Core Generation	Recommended LLM Size
Nvidia A100 Tensor Core GPU Ampere AI Accelerator	★ ★ ★	Data Centre	AI Training & HPC	PCIe, SXM	AI Training	Ampere	Gen4	2	3rd Gen	Large	View
Nvidia B200 Tensor Core GPU Blackwell AI Accelerator	★ ★ ★	Data Centre	AI Training & Inference	SXM	AI Training & Inference	Blackwell	Gen5	8	5th Gen	Multi-Node	View
Nvidia H100 Tensor Core GPU Hopper AI Accelerator	★ ★ ★	Data Centre	AI Training & Inference	NVL, PCIe, SXM	AI Training	Hopper	Gen5	3.35	4th Gen	Extra Large	View
Nvidia H200 Tensor Core GPU Hopper High-Memory AI Accelerator	★ ★ ★	Data Centre	AI Training & Inference	NVL, SXM	AI Training & Inference	Hopper	Gen5	4.8	4th Gen	Extra Large	View
Nvidia L40S GPU AI Inference & Graphics Accelerator	★ ★ ★	Data Centre	AI Inference & Training	PCIe	Mixed AI	Ada Lovelace	Gen4	0.86	4th Gen	Large	View
Nvidia L40 GPU Data Center AI & Visualisation GPU	★ ★ ★	Data Centre	AI Inference & Visualisation	PCIe	Mixed AI	Ada Lovelace	Gen4	0.86	4th Gen	Medium	View
Nvidia L4 GPU Low-Power AI Inference Accelerator	★ ★ ★	Data Centre / Edge	AI Inference & Video	PCIe	AI Inference	Ada Lovelace	Gen4	0.3	4th Gen	Small	View

Nvidia Graphics Card Deployment Scenarios and Industries

Data Centres

Data centre teams need one GPU estate that can support AI training, inference, analytics and HPC without low utilisation. Nvidia graphics cards give them a mix of broad-purpose and higher-performance options, plus shared-use features where they matter, so infrastructure can stay flexible as workloads change.

Media & Entertainment

Studios and content teams need to run rendering, video, AI and simulation in the same environment. Nvidia graphics cards support these mixed pipelines, helping teams balance visual work, generative AI and media processing without splitting everything into separate GPU pools.

Healthcare

Healthcare organisations often need a shared GPU platform for imaging, research, analytics and selective content generation. Nvidia graphics cards help teams allocate the right level of acceleration across projects, while keeping deployment practical for both central and space-constrained sites.

Finance

Financial teams want acceleration for modelling, fraud detection, forecasting and AI services without constant platform churn. Nvidia graphics cards provide a stable base for mixed workloads, with enough flexibility to support both established analytics estates and newer generative AI use cases.

Software Development

Development teams need hardware that can support building, testing and tuning AI, analytics and visual applications on one platform. Nvidia graphics cards give them a widely supported target for shared engineering work, helping simplify operations across current and future deployments.

Nvidia Graphics Card Management and Licensing Options

Shop All Nvidia Graphics Card Models

Browse our full range below, or contact our team for tailored configuration advice.

Nvidia A100 Tensor Core GPU Ampere AI Accelerator
Price range: £5,995.00 through £9,995.00 Ex. VAT
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Nvidia B200 Tensor Core GPU Blackwell AI Accelerator
Price range: £29,995.00 through £34,995.00 Ex. VAT
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View models This product has multiple variants. The options may be chosen on the product page
Nvidia H100 Tensor Core GPU Hopper AI Accelerator
Price range: £17,995.00 through £34,995.00 Ex. VAT
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Nvidia H200 Tensor Core GPU Hopper High-Memory AI Accelerator
Price range: £29,995.00 through £42,995.00 Ex. VAT
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Nvidia L4 GPU Low-Power AI Inference Accelerator
Price range: £1,895.00 through £2,495.00 Ex. VAT
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Nvidia L40 GPU Data Center AI & Visualisation GPU
Price range: £4,495.00 through £5,995.00 Ex. VAT
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Nvidia L40S GPU AI Inference & Graphics Accelerator
Price range: £5,995.00 through £7,495.00 Ex. VAT
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Nvidia Graphics Card FAQ

Why are Nvidia Graphics Cards often chosen instead of relying on CPU-only infrastructure for AI and visual computing?

CPU-only infrastructure can support lighter analytics and visual computing, but limitations usually appear once AI models, rendering, simulation or video processing begin demanding much higher parallel compute performance.

Nvidia Graphics Cards provide dedicated GPU acceleration for AI, visual computing and high-performance applications, helping IT teams process larger datasets and demanding environments more efficiently without relying entirely on larger CPU estates.

When is H100 a better fit than A100?

A100 remains a strong option for shared AI, analytics and HPC environments, especially where teams already rely on established Ampere-based infrastructure and broad workload flexibility.

H100 is better suited to modern generative AI environments, including large language models and newer training or inference projects that need higher throughput and stronger scaling than A100 typically provides.

What operating conditions make H200 the right fit instead of H100?

H100 delivers strong AI performance for many enterprise applications, but memory limitations can become more noticeable as model sizes, context windows and scientific datasets continue to grow.

H200 extends the Hopper platform with larger and faster HBM3E memory, making it better suited to memory-intensive AI and HPC environments where keeping more data closer to the GPU improves efficiency and reduces workflow bottlenecks.

Why would an organisation choose L40S instead of L40?

Graphics-focused GPUs can work well for rendering, digital twins and virtual workstation environments. Pressure usually increases once the same infrastructure also needs to support larger AI inference and generative AI operations.

L40S is designed for mixed environments that combine AI, rendering and media acceleration, helping teams consolidate more applications onto one GPU platform without moving into larger dedicated AI platforms.

When is L4 a better fit than larger Nvidia Graphics Cards?

Larger GPUs can provide more compute capability, but they also introduce higher power, cooling and space requirements that are not always practical for distributed or edge deployments.

L4 is designed for efficient inference, video processing and analytics in dense or compact servers. That makes it a strong fit for environments where server density, thermals and operational efficiency matter more than large-scale AI training performance, without significantly increasing rack power and cooling requirements.

Why do many organisations still invest in Nvidia Graphics Cards for shared enterprise environments?

Moving every application onto separate specialist infrastructure can increase operational complexity, hardware costs and underused resources.

Nvidia Graphics Cards allow IT teams to support AI, rendering, analytics, simulation and visual computing across shared GPU infrastructure, helping improve utilisation while simplifying platform management and future scaling.

Need a different solution?

If these options aren’t the right fit for your environment, we provide a wide portfolio of product series and solutions that may better suit your infrastructure. Explore below, or speak to our team and we’ll help you find the right match.

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