GPU Dedicated Servers With Nvidia, AMD & Tesla Video Cards

GPU dedicated servers are perfect for mining, streaming, and a lot more. Check out this review of GPU dedicated servers with Nvidia, AMD and Tesla video cards.

The recent ban on consumer grade GPUs in data centers is making it more expensive to run a data center. Why is that?

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In this article, we’ll look at some interesting facts on the growing demand for GPU Dedicated Servers.

How The Bubble Popped

Crypto miners expanded their operations exponentially, creating a shortage of GPUs. Nvidia, the biggest manufacturer of these components were happy to supply miners with all the equipment they could buy.

But then the price of Bitcoin took a dive. Miners found their operations unprofitable and flooded the market with low priced GPUs. Data centers started using these second-hand GPUs to save on hardware costs.

The unusually high demand that raised Nvidia’s revenue the year before, is now the cause of lagging sales.

But servers can run without GPUs, so why do so many data centers use them?

Do Servers Need GPUs?

A server for a small organization can get by with your average CPU. But data centers deal with big data. They need to process terabytes upon terabytes of data. For this, you need an Nvidia Server GPU.

Some of the benefits a server graphic card offers over a CPU server are:

• Compatibility with high-performance computing (HPC) applications. Over 550 HPC applications use GPUs to analyze quantum chemistry, molecular dynamics, and more. GPUs support deep learning which leads to the next benefit…
• GPUs and AI go hand in hand. Data centers are at the front of the AI industry. AI extends the reach of HPCs by adding the ability to comb through vast amounts of data.
• A single server running Tesla V100 GPUs can replace 60 CPU servers

But not all GPUs are equal. Nvidia’s ban on consumer grade components in data centers is a direct result of this. These cheaper parts were never designed for the volume a data center deals with. As a result, Nvidia decided not to honor warranties if these parts are being used in a data center.

So what’s the big deal? What makes the recommended Tesla V100 so much better equipped to handle big data? Let’s talk about the differences.

Nvidia Tesla VS Titan

Price makes switching to the Tesla a pain for data centers. The Tesla series of GPUs costs up to four times as much as the Titan. The Titan is the consumer-grade version of the Tesla designed for gaming.

The Titan is the industrial model suited for HPC programs. Let’s talk about what that means. To really understand what data centers are doing with all those GPU dedicated servers, we need to be clear on some basic vocabulary.

• Machine Learning. Machine learning is a subset of artificial intelligence (AI). It is the process of setting up a program, giving it some starter information, then letting it learn from incoming data. Machine learning is more hands-on, needing a model to base decisions on.
• Deep Learning. Deep learning is the next step after machine learning. It is the process of breaking down complex ideas into smaller, easier to organize pieces.

Deep learning comes up with solutions from scratch. It needs more raw data than machine learning to get to the same point since it has no initial model to go off of.

Data centers help meet the computing power that deep learning projects have to have. The process of deep learning is why Nvidia considers Titan GPUs insufficient. To keep your Nvidia server status up to speed you’ll need the most powerful GPU you can get.

Machine learning can be done on the lower end Titan but it is still common to outsource for better performance. Since data centers can’t use Titans, all machine and deep learning must use higher performing GPUs.

AI and Data Centers

AI and data centers maintain a symbiotic relationship. Data centers provide the computing power for AI and in turn, AI produces new applications for data centers.

Voice search and image recognition are both perfect examples of how we use AI every day. When you use voice search on your phone, that data is then organized by deep learning to aid in future searches.

Because AI has such a broad range of uses, many data centers are being built around serving the deep learning community. And you can’t have deep learning without GPU dedicated servers. Some of the ways deep learning is being used by data centers include:

• Energy Efficiency. Google uses its DeepMind AI program to save energy regardless of the weather. One of its data centers in the Midwest saw its AI-powered cooling system counterintuitively change settings during a tornado watch. When engineers analyzed the program’s decision, it made sense for that situation.
• Cyber Security. Traditional means of blocking hackers include restricting user privileges. With users coming and going, this method is vulnerable to hackers who take advantage of loopholes. A machine learning algorithm can be set to detect any deviation from normal use on the servers and immediately stop an attack.
• Splitting Up Server Workload. With the shift to serverless infrastructure, companies now only have to pay for the time their applications are running. AI is being used to distribute work among servers more efficiently, lowering computing times and reducing costs.

Demand for GPU Dedicated Servers Will Keep Growing

Nvidia has generously estimated the market for GPU dedicated servers to grow to $50 billion by 2023. The reason being that although GPUs can get expensive, they have much more processing power than an old-fashioned CPU.

Three GPUs can have up to 18,000 core processors. It would take over a thousand CPUs to match the computing power. And CPUs come nowhere near being powerful enough to handle deep learning. Using a CPU for deep learning would be like trying to go 0-60 mph on a horse.

AI may be power data centers, but your home setup still needs a human touch. Find out what power supply for your server by reading this article now.