AMD Instinct vs NVIDIA Tesla [EOL] comparison

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

AMD Instinct

503 Views
362 Comparison Views

NVIDIA Tesla [EOL]

Read 2 NVIDIA Tesla [EOL] reviews

100% willing to recommend

AMD Instinct

NVIDIA Tesla [EOL]

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

AMD Instinct

Average Rating

0.0

Number of Reviews

Ranking in other categories

Enterprise GPU (4th)

NVIDIA Tesla [EOL]

Average Rating

8.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Use AMD Instinct?

Share your opinion

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.

Read full review

See which vendors are best for you

Use our free recommendation engine to learn which Enterprise GPU solutions are best for your needs.

See recommendations

867,445 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

13%

Manufacturing Company

11%

Educational Organization

11%

Computer Software Company

13%

Comms Service Provider

11%

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

Ask a question

Earn 20 points

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.

See all answers

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.

See all answers

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...

See all answers

Comparisons

NVIDIA DGX Cloud vs AMD Instinct

Compared 74% of the time

GAUDI vs AMD Instinct

Compared 26% of the time

More AMD Instinct Competitors

Hailo-8 vs NVIDIA Tesla [EOL]

Compared 33% of the time

NVIDIA RTX Series vs NVIDIA Tesla [EOL]

Compared 26% of the time

Intel Movidius Myriad X VPU vs NVIDIA Tesla [EOL]

Compared 9% of the time

More NVIDIA Tesla [EOL] Competitors

Also Known As

Radeon Instinct, Radeon Instinct MI Series

No data available

Overview

Radeon Instinct family of server accelerator products brings in a new era of heterogeneous compute capabilities for Machine Intelligence and HPC systems by introducing an open approach to compute from the metal forward. Higher levels of datacenter performance and efficiencies are enabled through AMD’s introduction of world-class GPU technologies like the next generation “Vega” architecture and the Radeon Instinct’s open ecosystem approach to datacenter design through our ROCm software platform, support of various system architectures, and industry standard interconnect technologies.

AMD

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

See our list of best Enterprise GPU vendors.

We monitor all Enterprise GPU reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.