GAUDI vs NVIDIA Tesla [EOL] comparison

Intel and NVIDIA are both solutions in the Enterprise GPU category. Additionally, 100% of NVIDIA users are willing to recommend the solution.

GAUDI

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NVIDIA Tesla [EOL]

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100% willing to recommend

GAUDI

NVIDIA Tesla [EOL]

Review summaries and opinions

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Categories and Ranking

GAUDI

Average Rating

0.0

Number of Reviews

Ranking in other categories

Enterprise GPU (5th)

NVIDIA Tesla [EOL]

Average Rating

8.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

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reviewer2309676

Team Lead, High-Performance Computing (HPC) at a manufacturing company with 1,001-5,000 employees

Simplifies our processes and helps us handle complex computations effectively

The initial setup process for Tesla was straightforward for me since I had prior experience working with the product. Setting it up requires two people. The deployment process involves a two-step approach: hardware deployment and software development. After that, we use Ansible for automatic software installation. This includes getting the operating system in place using Foreman and adding necessary components like NVIDIA CUDA drivers. The deployment time varies based on the number of servers, but for around ten servers, it typically takes about two hours. We deploy them in parallel to streamline the process. Maintaining Tesla involves routine tasks like updating drivers and addressing security issues. We handle this by taking about 10% of our servers offline at a time, using a slow scheduler to ensure a controlled process.

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Computer Software Company

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Comms Service Provider

11%

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Questions from the Community

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What do you like most about NVIDIA Tesla?

The most valuable aspects of Tesla are its CUDA software framework, which boosts our computing capabilities, and NVIDIA's NGC cloud support.

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What is your experience regarding pricing and costs for NVIDIA Tesla?

The majority of our Tesla GPUs operate on bare metal servers without additional licensing costs.

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What needs improvement with NVIDIA Tesla?

While the current Tesla setup meets our needs well, it would be beneficial to see broader application support and compatibility with different workloads. The existing configuration handles our curr...

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Comparisons

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Overview

Gaudi is a processor that handles machine learning training workloads, and has 32GB of memory built-in, a memory bandwidth of 1TB per second, and consumes up to 200W. The only AI processor with Integrated RDMA over Converged Ethernet to provide scalability and lower total cost of ownership. Gaudi is designed for versatile and efficient system scale out and scale up with integrated on-chip RoCE RDMA, enabling high-performance inter connectivity.

Intel

NVIDIA Tesla [EOL] is designed for scientific computing tasks, data analytics, and high-performance computing. Its architecture enables efficient processing for demanding data-intensive operations, making it a vital component for advanced computational applications.

NVIDIA Tesla [EOL] offers a powerful platform for professionals seeking advanced computational capabilities. It excels in areas like machine learning, offering exceptional parallel processing which dramatically accelerates complex tasks. This makes it indispensable for environments requiring rapid data throughput and high-speed computations, ensuring optimal performance in research, finance, and engineering sectors.

What are the most valuable features of NVIDIA Tesla [EOL]?

High-Performance Capabilities: Enables faster processing for complex algorithms.
Scalability: Easily integrates into existing systems for enhanced computational power.
Energy Efficiency: Delivers powerful performance with optimized power consumption.

What benefits should users consider when reviewing NVIDIA Tesla [EOL]?

Cost Efficiency: Reduces computational time, saving resources and costs.
Enhanced Productivity: Improves processing speeds, increasing overall workload efficiency.
Robust Performance: Consistent results in high-demand scenarios, supporting critical applications.

NVIDIA Tesla [EOL] is widely used in industries such as finance and research, where high-speed data processing is critical. In finance, it facilitates rapid data analysis and modeling. In scientific research, it supports computational simulations and data-driven experiments, optimizing workflow and output accuracy.

NVIDIA

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