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NVIDIA DGX Cloud vs NVIDIA Tesla [EOL] comparison

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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

NVIDIA DGX Cloud
Average Rating
9.0
Number of Reviews
1
Ranking in other categories
AI Infrastructure (3rd)
NVIDIA Tesla [EOL]
Average Rating
8.6
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Featured Reviews

reviewer2309676 - PeerSpot reviewer
Team Lead, High-Performance Computing (HPC) at a manufacturing company with 1,001-5,000 employees
Versatile, well-built, and powerful
The initial setup of the DGX server was quite straightforward. We treated it like any other server during deployment. It went to the data center, where they set it up, placed it in the rack, and enabled it. The deployment process was familiar, using our standard tools like Foreman and Ansible. Since the operating system is supported, we didn't encounter any specific challenges. For deploying the DGX server, we typically need two people for software tasks and sometimes vendor assistance for hardware setup. The process takes about four hours, with NVIDIA firmware updates taking the most time (around two hours), and the rest dedicated to OS and Ansible deployment. Maintaining the DGX server is pretty straightforward. We treat it like any other server, with around 10% downtime, while the rest of the cluster remains up.
reviewer2309676 - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The most valuable thing about DGX Systems is their super-fast connection."
"The most valuable aspects of Tesla are its CUDA software framework, which boosts our computing capabilities, and NVIDIA's NGC cloud support."
"The ease of use is a significant advantage."
 

Cons

"One thing that could be better in DGX Systems is their power consumption."
"It would be beneficial to see broader application support and compatibility with different workloads."
"I believe there should be an effort to lower costs, especially considering the higher price of the latest update."
 

Pricing and Cost Advice

Information not available
"Generally, the price is affordable, but the most recent update comes with a notable increase in cost."
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Top Industries

By visitors reading reviews
University
11%
Manufacturing Company
10%
Comms Service Provider
10%
Financial Services Firm
8%
Comms Service Provider
19%
Financial Services Firm
11%
Educational Organization
9%
Computer Software Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Also Known As

NVIDIA DGX-1, DGX Cloud, NVIDIA DGX Platform
No data available
 

Overview

 

Sample Customers

Open AI, UC Berkley, New York University, Massachusetts General Hospital
Information Not Available