Nvidia GPU family for Deep Learning


Deep Learning is a term that has taken over the tech jargon in recent times. However, not everyone who has come across the term understands it in detail. Here is a consolidated overview of the key concepts before going into the contribution of the NVIDIA GPU family for Deep Learning.

Difference between CPU and GPU

CPU vs GPU | Definition and FAQs | HEAVY.AI

GPUs are advanced processing units in comparison to CPUs or the Central Processing Units. Central Processing Unit is, in fact, a generalized processor that is programmed to execute a wide variety of tasks. GPU (Graphics Processing Unit) is a specialized processing Unit with enhanced capabilities of Mathematical Computation, programmed properly for machine learning tasks.

The work of a Central Processing Unit or CPU is central to the working of a computer. Every single action in a computer system is done by the core command of the Central Processing, or CPU is central to the working of a computer. Every single action in a computer system is carried out by the core command of the Central Processing Unit. CPU, the computer’s brain, is adept at taking information, calculating the information, and aligning it with what and where it needs to. Computers work/operate through the logical frameworks of the CPU. The CPU consists of a few standard components, and the following are included in it.


The main architecture of the CPU is called the Cores. Cores function through what is regarded as the instruction cycle; here, instructions are pulled from memory, decided into processing language (decoding), and executed via cores. With the multi-core CPU’s proliferation, there has been an immense power amplification with the cores.


The cache is superfast memory built into the CPU or in CPU-specific motherboards in an attempt to facilitate quicker access to data. The CPU is currently using that. Cache memory’s work is to amplify the speed of the CPU, which is faster than the fastest RAM.

Memory Management Unit

The MMU or the Memory Management Unit controls the movement of data between RAM and the CPU during the instruction cycle.

Control Unit

The control unit is the clock rate determines the frequency at which the CPU generates electric pulses and determines the brisk rate at which the CPU will conduct the functions of the computer at a fast pace. The higher the CPU clock rate, the faster it will run. All these components work together, providing an environment where high-speed multitasking occurs. The CPU cores keep switching rapidly between hundreds of different tasks per second. More all at the same time.

What is GPU?

What's a GPU? Everything You Need to Know - The Plug - HelloTech

As briefly discussed previously, GPU or Graphical Process Units are a type of processor made up of more specialized cores. These cores develop massive intensity performance, a processing task that can be divided and processed across many cores.

GPUs have been a key component in sparking the AI boom, becoming a key part of modern Super Computers in gaming and pro-graphics.

Talking of Artificial Intelligence, can we be far from discussing Machine Learning and Deep Learning.

How are Artificial Intelligence, Machine Learning, and Deep Learning interlinked

Artificial Intelligence Machine Learning and Deep Learning: A fruitful chat on buzzwords | by Imanol Goicoechea | Bedrock — Augmented Human Intelligence | Medium

These are concepts that are interconnected. One can visualize these in the form of concentric circles, the largest being AI. Then comes machine learning-which blossomed after a point, and finally, Deep Learning forms the last circle.

Talking of Deep Learning

Why is everyone talking about Deep Learning? | by Nitish Bhardwaj | codeburst

Talking of Deep Learning, these are algorithms for machines to observe patterns. Computers acquire uncanny capabilities through deep Learning. E.g., the ability to recognize speech and translate it to another language.

NVIDIA GPU family for Deep Learning ensures executions of top-notch Deep Learning Models. The advancements in Artificial Intelligence have led to sophisticated Deep Learning algorithms thanks to the implementation of GPUs like the Nvidia Deep GPU family for Deep Learning. Deep Learning is an important concept that has helped elevate performance across industries. Implementation of Deep Learning

Deep learning relies on GPU acceleration for both the purposes of training and inference. NVIDIA can deliver GPU acceleration everywhere and anywhere it is needed—it delivers to data centers, laptops, desktops, and the world’s fastest supercomputers. If your data happens to be in the cloud, you will find NVIDIA GPU Deep Learning to be available on services from Amazon, Google, IBM, Microsoft, and many others.

The world of computing is going through a tremendous change. With deep learning and AI, computers learn to write their software. NVIDIA GPU Deep Learning creates proficient Deep Learning Models that can enable and facilitate the solutions of complex problem structures from across different domains and categories of businesses. As machines start learning by observing patterns and data structures, lives will become a lot simpler and easier in ways that were unimaginable previously; right from healthcare to self-driving cars, deep learning is creating a world of magic possible for us.


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