NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A
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NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A
The NVIDIA Tesla M10 GPU accelerator delivers exceptional user experiences, enables high user density, and provides cost-effective desktop virtualization.
NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A
HP

Numéro d'article: 9714628

NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A

90% des répondants recommanderaient ceci à un ami

Numéro d'article: 9714628

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The NVIDIA Tesla M10 GPU accelerator delivers exceptional user experiences, enables high user density, and provides cost-effective desktop virtualization.
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Ce qui se démarque

High Performance
The NVIDIA Tesla M10 delivers exceptional performance for virtual desktops and GPU-accelerated applications, enabling seamless user experiences in demanding environments.
Virtualization Ready
Optimized for virtualization, the Tesla M10 allows multiple users to share GPU resources, efficiently supporting a large number of concurrent sessions without sacrificing performance.
Energy Efficient
With its advanced architecture, the Tesla M10 provides high computational power while maintaining energy efficiency, reducing overall operational costs for businesses looking to maximize productivity.

Détails du produit

Get the NVIDIA Tesla M10 GPU accelerator for high user density virtualized desktops and applications. Deliver great experiences at an affordable cost. Shop now at Ubuy Algeria
Graphics CoprocessorNVIDIA Tesla M10
Graphics Card Ram32 GB
Video Output InterfaceDisplayPort, DVI
Graphics Ram TypeDDR5 SDRAM
Compatible DevicesDesktop
Graphics Card InterfacePCI Express
Memory Clock Speed2600 MHz
Display Maximum Resolution4096x2160
Display Resolution Maximum4096x2160
Item Dimensions L x W12"L x 5"W (30.5 cm)
BrandHP
Video ProcessorNVIDIA
Antenna LocationSecurity
Built-In MediaNVIDIA GPU computing processor
Model NameTesla M10
Graphics DescriptionNVIDIA Tesla M10 GPU with 32 GB DDR5 SDRAM (High-performance computing, AI, and graphics workloads)
Unit Count1 Count
Global Trade Identification Number00190017096537
ManufacturerNVIDIA
UPC190017096537 070090426097
Item Weight2.2 pounds (1 kg)
Item Type NameTesla M10 GPU Computing Processor
Model NumberQ0J62A
Mfr Part NumberQ0J62A
Graphics Ram Size32 GB
Graphics Processor ManufacturerNVIDIA

À qui est-ce destiné ?

Suitable For
  • Virtual Desktop Infrastructure

    Ideal for organizations using VDI, providing multiple users with seamless access to GPU resources for enhanced performance.

  • Data Center Applications

    Suitable for cloud computing and data center tasks, handling extensive workloads effectively with power-efficient design.

  • Machine Learning Tasks

    Perfect for ML environments, accelerating model training processes and complex computations needed for AI development.

Not Suitable For
  • Casual Gamers

    Not suitable for casual gaming due to its focus on enterprise and computational tasks rather than gaming performance.

DESCRIPTION DU PRODUIT

NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is the NVIDIA Tesla M10 GPU used for?

    répondre: The NVIDIA Tesla M10 GPU is primarily designed for cloud-based graphics processing and virtualization. It excels in delivering high performance for tasks such as machine learning, deep learning, and running virtual desktops. With its ability to support multiple users, it is perfect for organizations looking to maximize their computational resources for applications that require heavy graphical processing, thereby enhancing productivity and efficiency.
  • question: How does the NVIDIA Tesla M10 compare to other GPUs?

    répondre: The NVIDIA Tesla M10 stands out due to its focus on parallel processing and virtualization. Unlike consumer-grade GPUs, the M10 delivers optimized performance for multi-user environments. It supports up to 64 simultaneous users, making it ideal for large enterprises or educational institutions that need robust graphics capabilities for applications like computational fluid dynamics or simulations, ensuring users do not experience lag or bottlenecks.
  • question: What type of projects is the Tesla M10 suited for?

    répondre: The Tesla M10 is particularly well-suited for projects that require intensive graphical computations such as 3D rendering, video transcoding, and advanced simulations in engineering. Businesses involved in AI model training or scientific research will benefit from its ability to process vast datasets quickly, making it invaluable for users who rely on high-performance computing for innovation and development.
  • question: What system requirements are needed for the Tesla M10 GPU?

    répondre: To effectively utilize the NVIDIA Tesla M10 GPU, a compatible server with PCIe 3.0 slots is required. Additionally, it’s crucial to have adequate cooling solutions and a robust power supply. For optimal performance, pairing the M10 with a powerful CPU and sufficient RAM can vastly enhance the processing power available for demanding applications, proving essential for tech businesses and research facilities.
  • question: Can the Tesla M10 support gaming applications?

    répondre: While the Tesla M10 is not primarily designed for gaming, it can be adapted for high-performance gaming applications in virtualized environments. However, users should consider alternatives that are specifically optimized for gaming to ensure the best experience in terms of graphical fidelity and frame rates. It’s more focused on enterprise environments than individual gaming preferences.
  • question: Is the Tesla M10 suitable for machine learning tasks?

    répondre: Yes, the NVIDIA Tesla M10 is highly suitable for machine learning tasks. Its advanced GPU architecture allows for accelerated computation of complex algorithms and data sets, making it a valuable asset for data scientists and researchers. The ability to support multiple users simultaneously also enhances collaborative machine learning efforts within organizations, facilitating collective intelligence and innovation.
  • question: What is the memory capacity of the M10 GPU?

    répondre: The NVIDIA Tesla M10 GPU features 32 GB of GDDR5 memory, which is distributed across its four GPU cores. This substantial memory capacity enables it to handle large datasets efficiently, making it ideal for applications that require robust performance for data analytics, visualization, and complex simulations. This ensures that users can work with high-resolution models or extensive datasets without performance issues.
  • question: How does GPU virtualization work with the Tesla M10?

    répondre: GPU virtualization on the NVIDIA Tesla M10 allows multiple virtual machines to share the GPU resources concurrently. This technology enables different users to run graphics-intensive applications within their own virtual environments, effectively maximizing resource utilization and cost efficiency. It’s particularly useful in educational institutions or corporate settings where multiple users need access to powerful GPUs simultaneously.
  • question: What type of cooling solutions are recommended for the Tesla M10?

    répondre: Effective cooling solutions are critical when using the NVIDIA Tesla M10 GPU, as high-performance components tend to generate significant heat. A server with robust airflow and additional cooling systems, such as liquid cooling or high-CFM fans, can prevent overheating and ensure sustained performance. This is essential in server rooms or data centers where multiple GPUs operate under heavy loads for extended periods.
  • question: Where can I buy NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A?

    répondre: You can buy the NVIDIA Tesla M10 GPU Computing Processor Graphic Cards Q0J62A on Ubuy. Ubuy is a reliable online platform that often features a variety of high-tech components including the Tesla M10, making it a convenient option for professionals and enterprises looking to enhance their computational capabilities.

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Avantages

  • High performance for virtualization
  • Supports multiple GPU sessions
  • Energy efficient design
  • Robust software compatibility
  • Excellent for AI workloads

Les inconvénients

  • Higher initial investment

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