NVIDIA Graphics Cards for AI, Servers, Workstations, and High-Performance Computing
NVIDIA Server GPU - All Products:
Gigabyte
GV-N5090GAMING OC-32GD Gigabyte Nvidia GeForce RTX 5090 32GB GDDR7 GPU
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PNY
VCNRTXA4000-SB PNY Nvidia RTX A4000 16GB GDDR6 1560MHz 448GT/s GPU
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VCG507012TFXPB1 PNY Nvidia GeForce RTX 5070 12GB GDDR7 2510MHz 672GT/s Desktop GPU
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Frequently Asked Questions about NVIDIA Server GPU:
NVIDIA Graphics Cards for Different Requirements
The NVIDIA portfolio includes graphics cards and GPU accelerators for various professional applications. While GeForce and RTX graphics cards are frequently used in workstations, visualization, and content creation, data center GPUs based on Ampere, Hopper, Blackwell, and Rubin architectures are primarily used in AI, HPC, GPU computing, and data center environments.
| Product Family / Architecture | Typical Focus |
|---|---|
| NVIDIA Ada Lovelace | Professional Visualization, Rendering, and AI-Assisted Workflows |
| NVIDIA Ampere | AI, GPU Computing, HPC, and Virtualization |
| NVIDIA Blackwell | Generative AI, Inference, HPC, and Data Centers |
| NVIDIA GeForce | Rendering, Content Creation, and Local AI Applications |
| NVIDIA Hopper | AI Training, Deep Learning, Large Language Models, and HPC |
| NVIDIA Rubin | AI Training, Inference, Large Language Models, and HPC |
| NVIDIA RTX | CAD, CAE, BIM, Rendering, and Professional Workstations |
| NVIDIA Tesla | GPU Computing, Data Centers, HPC, and Virtualization |
| NVIDIA Titan | Research, Deep Learning, and Compute Applications |
Architecture and Functionality of NVIDIA Graphics Cards
Modern NVIDIA graphics cards are based on specialized GPU architectures optimized for different applications. Ampere, Ada Lovelace, Hopper, Blackwell, and Rubin differ in areas such as computing performance, memory bandwidth, AI acceleration, energy efficiency, and application. At the core of every NVIDIA GPU are specialized compute units that enable massive parallelization of workloads:
- CUDA Cores: For general parallel processing, GPU computing, and scientific calculations.
- Tensor Cores: For AI, machine learning, deep learning, and matrix calculations.
- Ray Tracing Cores: For real-time ray tracing, rendering, and professional visualization.
- VRAM and Cache: For fast access to large amounts of data, models, and textures.
- NVLink: For fast GPU-to-GPU communication in supported multi-GPU systems.
This architecture makes it possible to execute compute-intensive tasks significantly faster than with traditional CPU-based systems. NVIDIA graphics cards can provide substantial performance advantages, particularly for AI, simulation, rendering, and data-intensive analytics.
NVIDIA Graphics Cards as Accelerators for Modern IT Infrastructures
Modern IT infrastructures need to process ever-growing amounts of data in less time. NVIDIA graphics cards reduce the workload on traditional CPUs by offloading highly parallel calculations to specialized GPU architectures. This enables compute-intensive applications to operate more efficiently and scale more effectively. NVIDIA GPUs have become key accelerators particularly in the areas of artificial intelligence, High Performance Computing, virtualization, rendering, and data analytics. Businesses benefit from shorter computation times, higher data throughput, and more efficient use of existing resources.
- AI Training and AI Inference
- Machine Learning and Deep Learning
- Large Language Models (LLM)
- High Performance Computing (HPC)
- GPU Computing
- Data Analytics
- Rendering and Visualization
- Cloud and Virtualization Platforms
Performance, Memory, and Technical Features of NVIDIA Graphics Cards
The performance of modern NVIDIA graphics cards results from the interaction of architecture, GPU computing performance, memory bandwidth, GPU memory, clock speed, and specialized acceleration units. Memory architecture plays a particularly important role in AI applications, rendering, simulations, and High Performance Computing. Important technical performance characteristics include:
- VRAM Capacity: Crucial for large AI models, high-resolution textures, and extensive datasets.
- Memory Bandwidth: Determines how quickly data can be transferred between the GPU and memory.
- GPU Computing Performance: Important for parallel processing, simulations, rendering, and compute workloads.
- Energy Efficiency: Particularly relevant for server, workstation, and data center environments.
- Multi-GPU Capability: Important for scalable systems with multiple graphics cards or GPU accelerators.
High computing performance, memory bandwidth, and GPU memory capacity are important factors for data-intensive applications. However, which NVIDIA graphics card is best suited depends on the specific workload and its requirements.
Overview of NVIDIA GPU Architectures and Product Families
NVIDIA Ada Lovelace
The NVIDIA Ada Lovelace architecture provides modern ray tracing and AI acceleration and is particularly suitable for professional visualization, rendering, workstations, and AI-assisted workflows.
NVIDIA Ampere
The Ampere architecture is designed for AI, GPU computing, virtualization, and HPC applications. It forms the basis of many powerful enterprise and server solutions.
NVIDIA Blackwell
Blackwell is a current NVIDIA architecture for generative AI, inference, High Performance Computing, and scalable data center environments. It was specifically developed for modern AI and compute workloads.
NVIDIA Rubin
NVIDIA Rubin is a current NVIDIA GPU architecture for demanding AI, inference, training, and HPC workloads. Rubin GPUs offer up to 288 GB of HBM4 memory with up to 22 TB/s of memory bandwidth and support sixth-generation NVLink for high-bandwidth multi-GPU systems.
NVIDIA GeForce
NVIDIA GeForce graphics cards are frequently used for content creation, rendering, visualization, and local AI workloads. They provide high graphics performance and are suitable for systems where strong GPU performance at an economical price is required.
NVIDIA Hopper
Hopper was developed for AI training, deep learning, Large Language Models, and scientific simulations. The architecture is particularly relevant for demanding AI and HPC workloads.
NVIDIA RTX
NVIDIA RTX graphics cards and professional RTX GPUs combine CUDA Cores, Tensor Cores, and Ray Tracing Cores. They are particularly suitable for professional workstations, CAD, CAE, BIM, rendering, visualization, and AI-assisted workflows.
NVIDIA Tesla
NVIDIA Tesla accelerators were developed for GPU computing, virtualization, High Performance Computing, and data centers. Although newer product lines are now more prominent, Tesla GPUs remain relevant for many existing server and data center environments.
NVIDIA Titan
NVIDIA Titan graphics cards are a former NVIDIA product family that was used in research, development, and compute environments, among other applications.
Which NVIDIA Graphics Card Is Suitable for Which Application?
Which NVIDIA graphics card is the right choice depends heavily on the specific workload. Different GPUs are suitable for professional visualization and CAD than for AI training, HPC, or GPU virtualization.
| Application | Suitable NVIDIA Graphics Cards / Product Families |
|---|---|
| CAD, CAE, BIM, and Professional Visualization | NVIDIA RTX PRO, NVIDIA Ada Lovelace |
| Rendering and Content Creation | NVIDIA GeForce, NVIDIA RTX, NVIDIA RTX PRO |
| AI Development and Local AI Workloads | NVIDIA RTX, NVIDIA RTX PRO, as well as Ampere- and Blackwell-based GPUs |
| Deep Learning and AI Training | NVIDIA Ampere, NVIDIA Hopper, NVIDIA Blackwell, NVIDIA Rubin |
| High Performance Computing (HPC) | NVIDIA Ampere, NVIDIA Hopper, NVIDIA Blackwell, NVIDIA Rubin |
| GPU Computing and Scientific Calculations | NVIDIA Ampere, NVIDIA Hopper, NVIDIA Blackwell, NVIDIA Rubin |
| Servers, Virtualization, and Data Centers | NVIDIA Ampere, NVIDIA Hopper, NVIDIA Blackwell, NVIDIA Rubin |
NVIDIA Graphics Cards for Servers, Workstations, and Data Centers
In professional environments, NVIDIA graphics cards are used in workstations as well as servers and data centers. While workstation GPUs are frequently used for visualization, CAD, rendering, and development, scalability, stability, memory bandwidth, and multi-GPU capability are key priorities in server environments. The following factors are particularly important for server and data center environments:
- Compatibility with motherboard, CPU, and chassis
- Adequate power supply and cooling
- Appropriate driver and software support
- Sufficient VRAM for workloads and models
- Scalability for multi-GPU setups
- Integration into existing server and storage infrastructures
An NVIDIA graphics card can only reach its full potential when it is optimally matched with the CPU, RAM, storage, networking, and cooling.
Why Buy NVIDIA Graphics Cards from Server-Hardware?
NVIDIA graphics cards are powerful components, but they need to match the respective system environment. Server-Hardware supports businesses, system integrators, research institutions, and data centers in selecting suitable NVIDIA GPUs for workstations, servers, AI, rendering, GPU computing, and High Performance Computing.
- ISO 9001:2015 Certified Processes: Verified processes for quality, consulting, and project execution.
- Individual Project Consulting: Support in selecting suitable NVIDIA graphics cards for specific workloads.
- Compatibility Check: Verification of server, motherboard, CPU, power supply, cooling, and driver requirements.
- GPU Server Configuration: Support for high-performance systems for AI, HPC, rendering, and virtualization.
- Attractive Volume Discounts: B2B conditions for businesses, resellers, system integrators, and project customers.
- 24/7 Technical Support: Support for technical questions, configuration, and integration.
- Warranty Extensions of up to 6 Years: Additional security for long-term IT projects.
- Free Shipping: Fast and cost-effective delivery of your hardware.
Whether AI, rendering, workstations, GPU computing, or High Performance Computing – the right NVIDIA graphics card determines the performance, scalability, and future-readiness of your IT infrastructure. Get individual advice and find the right NVIDIA GPU solution for your requirements.