NVIDIA 699-22080-0200-520 Tesla Pci Dual Gpu K80 24GB

( Brand: Nvidia ), ( Type: Server Options ), ( Manufacturer Part Number: 699-22080-0200-520 U )

Review NVIDIA Tesla K80 24gb Pci e Dual Gpu

The NVIDIA Tesla K80 is a powerful and versatile graphics processing unit (GPU) designed for high-performance computing, data analytics, and scientific research. This particular model, with the product ID 699-22080-0200-520, is a dual GPU version, featuring two NVIDIA K80 GPUs on a single PCI-E interface.

The Tesla K80 is equipped with 4896 CUDA cores, a base clock speed of 700 MHz, and a boost clock speed of 875 MHz. It boasts an impressive 24 GB of GDDR5 memory, offering ample bandwidth for large datasets and complex computations. The GPU's architecture, based on the Kepler generation, ensures efficient and reliable performance, making it an ideal choice for a wide range of applications.

The dual GPU configuration of the NVIDIA Tesla K80 24GB PCI-E Dual GPU increases its computing power. Each GPU can run multiple tasks simultaneously, allowing for significant improvements in performance and throughput. This makes it an excellent choice for tasks such as deep learning, machine learning, computational fluid dynamics, and other high-performance computing applications.

In terms of connectivity, the Tesla K80 offers 3 DisplayPort 1.2 outputs and 1 HDMI 1.4a output, allowing for the connection of multiple high-resolution monitors. It also features NVLink technology, which enables seamless communication between GPUs in a single system, further enhancing performance and reducing power consumption.

In conclusion, the NVIDIA Tesla K80 24GB PCI-E Dual GPU is a high-performance GPU designed for demanding computing tasks. Its dual GPU configuration, combined with its large amount of GDDR5 memory and powerful architecture, makes it an excellent choice for applications such as deep learning, machine learning, and high-performance computing. With its wide range of connectivity options and energy-efficient design, it is a reliable and versatile solution for those seeking to push the boundaries of what is possible in computational performance.

1. High performance: The Tesla K80 is a powerful GPU with 4,896 CUDA cores and 24GB of GDDR5 memory, making it ideal for data science, machine learning, and high-performance computing tasks.

2. Dual GPUs: The Tesla K80 is a dual GPU card, which can provide even more computational power and increase the speed of parallel processing.

3. Support for CUDA, OpenCL, and other programming frameworks: The Tesla K80 supports popular programming frameworks such as CUDA, OpenCL, and cuDNN, making it easy to develop and deploy applications for data science and machine learning.

4. Reliability and durability: NVIDIA is known for its high-quality GPUs, and the Tesla K80 is no exception. It has a robust design and is built to withstand heavy use in data centers and high-performance computing environments.

1. High cost: The Tesla K80 is a high-end GPU, and its price reflects its performance and features. It may be too expensive for some users who only need a GPU for gaming or basic computing tasks.

2. Power consumption: The Tesla K80 is a power-hungry GPU, requiring a minimum of 300W of power and up to 375W under load. This can increase energy costs and may require a more powerful power supply.

3. Limited compatibility: The Tesla K80 is designed for high-performance computing and data science applications, and may not be compatible with all software and hardware. Users may need to modify their systems or applications to work with the Tesla K80.

Conclusion: The NVIDIA Tesla K80 24GB is a powerful and reliable GPU that is ideal for data science, machine learning, and high-performance computing tasks. Its dual GPUs and high-performance CUDA cores make it a great choice for users who need fast and efficient parallel processing. However, its high cost and power consumption may make it a less attractive option for users who only need a GPU for basic computing tasks or gaming.

Recommendation: If you are a data scientist, machine learning engineer, or high-performance computing researcher, the Tesla K80 24GB is an excellent choice. Its high performance and reliability make it an ideal tool for your work. However, if you are a casual gamer or only need a GPU for basic computing tasks, there may be more cost-effective options available. Consider your specific needs and budget before making a decision.

Details:

NVIDIA TESLA K80 24GB PCI-E DUAL GPU.: 699-22080-0200-520 GPU Specifications: Brand, Shipping Weight2.5kg, width 30cm, height 15cm, length 25cm, . External cables and rails are not included unless specified. All refurbished items are tested by our engineers, in the case of systems and parts, using OEM manufacturers standard software to post. We buy excess new and used servers, storage networking systems parts.

Decommission, secure data wipe and recycle services also available. CTO units are working minus any RAM, Drives CPU or Raid Card. Tape drives are further tested and at least one or both of the following, manufacturer software test Back Up Exec. These may not be an accurate image of the item for sale.

Hard drives and tape are further tested with at least one or more of the following processes; Blanca, SMART test Raid Initialisation. They include the chassis, motherboard, and 1x PSU only.

part #: 699220800200520 prices

• $99.99-$269.99

specifications serversappy:

• brand: Nvidia
• type: Server Options
• manufacturer part number: 699-22080-0200-520 U

shipping serversappy:

• cost: $9.00
• class: Economy, Expedited
• service: Economy, Expedited
• transit: 7-13 Days
• type: Fixed

returns serversappy:

• policy: No Returns

payments serversappy:

• wallet: Google-pay
• credit card: Visa
• other

seller serversappy:

• # reviews: 1697
• rating: 94.1%
• city: Torrance, California

offer serversappy:

• started: November 2, 2024
• sold: 0
• options: Ship-to-home
• best offers: True
• availability: In Stock
• quantity: 7

general serversappy:

• Enterprise Networking, Servers > Server Components > Other Ent Server Components
• condition: Used

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