GPU Servers
NVIDIA RTX PRO 6000 Blackwell Server Edition
96 GB of GDDR7 memory per GPU with up to 8 GPUs per server for unprecedented visualisation, rendering and inference power.
Ключевые характеристики
Подходящие задачи
Rendering Pipelines
Accelerate complex rendering with memory-rich, parallelised GPU configurations.
Технические характеристики
| Form Factor | 4U Rackmount |
|---|---|
| CPU | 2x AMD EPYC 9005 |
Accelerate professional visualization, AI, inference, and GPU-accelerated workloads with the NVIDIA RTX PRO 6000 Blackwell Server Edition. Built on the NVIDIA Blackwell architecture, this enterprise-class GPU is designed for data-center and server environments requiring a powerful combination of AI performance, graphics capabilities, large GPU memory, and energy-efficient acceleration.
The NVIDIA RTX PRO 6000 Blackwell Server Edition provides a versatile foundation for organizations running modern AI inference, generative AI, machine learning, professional visualization, virtual workstations, digital twins, simulation, rendering, and technical computing.
Built for Enterprise AI and Professional Workloads
Unlike GPUs designed exclusively for AI training, the RTX PRO 6000 Blackwell Server Edition is positioned as a versatile accelerator for a broad range of professional and enterprise workloads. It can help consolidate AI and graphics applications onto a common GPU infrastructure.
Typical applications include:
Generative AI inference
Large language model (LLM) inference
AI model development and fine-tuning
Machine learning
Professional visualization
3D rendering
Digital twins
Engineering and simulation
Virtual workstations
Omniverse applications
Media and entertainment workflows
Scientific and technical computing
GPU-accelerated enterprise applications
NVIDIA Blackwell Architecture
Powered by the NVIDIA Blackwell architecture, the RTX PRO 6000 Server Edition is designed to deliver high-performance acceleration for modern AI and graphics workloads.
Blackwell-based technology provides a strong foundation for organizations processing increasingly complex models, high-resolution visual content, 3D environments, and compute-intensive professional applications.
Large-Memory GPU for AI and Graphics
With substantial onboard GPU memory, the RTX PRO 6000 Blackwell Server Edition is designed for workloads where GPU memory capacity can be a critical performance factor. Larger models, complex 3D scenes, high-resolution assets, and professional applications can benefit from having more data available directly on the accelerator.
This makes the GPU well suited to AI inference, visualization, rendering, simulation, and other memory-intensive workloads running in enterprise server environments.
Designed for Data Center Deployment
The Server Edition is optimized for integration into professional GPU servers and enterprise infrastructure. Organizations can deploy RTX PRO 6000 Blackwell GPUs to support shared GPU resources, remote visualization, AI services, and accelerated professional applications.
This flexibility makes the platform suitable for enterprise data centers, cloud service providers, engineering organizations, media companies, research institutions, and professional design teams.
Key Highlights
NVIDIA RTX PRO 6000 Blackwell Server Edition
NVIDIA Blackwell architecture
Enterprise-class server GPU
Designed for AI inference and generative AI
Suitable for LLM inference and machine learning
Professional visualization and 3D rendering
Digital twins, simulation, and technical computing
GPU-accelerated virtual workstations
Large GPU memory capacity for demanding workloads
Designed for data-center and enterprise deployment
Versatile accelerator for AI and professional graphics
Whether you need to accelerate enterprise AI inference, LLM applications, professional visualization, rendering, simulation, or digital-twin workloads, the NVIDIA RTX PRO 6000 Blackwell Server Edition provides a versatile high-performance GPU platform designed for modern data-center environments.
Частые вопросы
Do you provide GPU servers for AI model training and inference?
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