Saitech Inc offering GPU’s for Deep & Machine Learning

Industry: Information Technology

Saitech Inc offering GPU solutions for Deep & Machine Learning

California (PRUnderground) July 1st, 2021

Saitech Inc is an authorized partner Reseller for NVidia GPU’s enhancing Machine learning and Artificial Intelligence projects.

Machine learning is a subset of artificial intelligence function that provides the system with the ability to learn from data without being programmed explicitly.

Machine learning is basically a mathematical and probabilistic model which requires tons of computations. It is very trivial for humans to do those tasks, but computational machines can perform similar tasks very easily.

In this post, we will cover the best GPUs for machine learning available in the market. NVIDIA GPU Series 2021 is recommended by technical experts when it comes to choosing a performing GPU for an enterprise data center. Let’s dig into the topic and get to know more about it. In 2021, HP launches new laptops with wireless AirPods and upgrade NVIDIA GeForce graphics.

Important Facts:

GPUs are faster than CPUs and suitable for the computation of AI and deep learning applications. GPUs are optimized for training artificial intelligence and deep learning models as they can multiprocess neural networks simultaneously.

GPUs are a safer bet for fast machine learning because, at its heart, data science model training consists of simple matrix math calculations, the speed of which may be greatly enhanced if the computations are carried out in parallel. (Source: Reddit)

GPUs are faster for computing than CPUs. With large datasets, the CPU takes up the task sequentially which is not recommended for deep learning. GPUs are equipped with VRAM (Video RAM) memory. Thus, the CPU’s memory can be used for other tasks.

Is GPU Necessary For Deep Learning?

So, if you are planning to work on other ML areas or algorithms, a GPU is not necessary. If your task is a bit intensive, and has a manageable data, a reasonably powerful GPU would be a better choice for you. A laptop with a dedicated graphics card of high end should do the work.
The concept of deep learning involves mathematical calculations and extreme operations, including matrix multiplication. It is a field that depends on the kind of GPU you plan on using for your calculations.

So, we can consider the GPU as an integral device for actualizing the concept of deep learning. Choosing a high-performing GPU will not only help you compute fast but also help you achieve outstanding performance. You must be able to design the kind of products you plan on using Artificial Intelligence.

A distinctive GPU will help you obtain a high-quality image with HD definition. Investing in purchasing a top-quality GPU is a smart move for acquiring the best outcomes especially with deep learning.

You can process the images and videos at a much quicker rate and increase the efficiency of your CPU. Users need to understand that their workflow can become sluggish when not using a prominent GPU.

3 Algorithm Factors Affecting GPU Use

In our experience helping organizations optimize large-scale deep learning workloads, the following are the three key factors you should consider when scaling up your algorithm across multiple GPUs.

  • Data parallelism
  • Memory use
  • Performance of the GPU 

NVIDIA TITAN XP Graphics Card (900-1G611-2530-000)

NVIDIA Titan RTX Graphics Card

PNY NVIDIA Quadro RTX 8000

NVIDIA GeForce RTX 2080 Graphics Card

NVIDIA Tesla V100

Nvidia Tesla P100

NVIDIA Tesla K80

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About Saitech Inc

Saitech Inc is an innovative value-added supplier for information technology hardware, software, supply chain services to support cloud computing, data center management, data storage, rugged mobility devices, marine electronics, and office equipment. Saitech Inc provides a total solution to IT acquisitions by providing multi-vendor hardware and software along with significant pre-sale and post-sale services. We provide significant value-added services consisting of configuration consulting and design, systems integration, installation of multi-vendor computer equipment, customization of hardware, product technical support, maintenance, and end-user support.

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