How The GPU Industry Is Priming For A Fattening Crypto Mining Market

Nvidia is releasing a driver update along with RTX 3060 GPUs, enabling the cards to detect the Ethereum crypto mining algorithm.

NVIDIA Releases TLT 3.0 To Build AI With Faster Time-To-Market

NVIDIA releases pre-trained models and Transfer Learning Toolkit 3.0 to accelerate developers' journey from training to deployment.

Why Benchmarking TinyML Systems Is Challenging

TinyML systems have the potential to offer greater responsiveness and privacy compared to traditional ML devices.

Installing Pytorch with CUDA support on Windows 10

Configure a Conda environment in Pycharm to enable the use of CUDA

6 Best Top-End Workstations For Data Scientists

The driving demand for data science has led companies to build workstations that can handle the huge collections of data.

Enabling Edge AI Through Future Ready Software Development Kit

Smart Speaker like Echo, Google Nest, is one such example of Edge AI solutions in the consumer electronics sector.

Can NVIDIA’s A100 80GB GPU Extend Its Lead On MLPerf Benchmark?

In an attempt to further unlock the immense potential of AI for supercomputing, NVIDIA launched an 80GB version of A100 GPU.

GPU-Optional Python

Write code that exploits a GPU when available and desirable, but that runs fine on your CPUs when not

Faster video stitching with OpenGL

Faster video stitching with OpenGL - OpenCV comes with an advanced sample implementation which produces great results on still images, however, using this program on every single frame of video streams is unsurprisingly extremely slow.

AMD Enters Major Leagues, Big Tech Big Gains & More: Weekly Top News

AMD’s goal of chipping off market share from Intel is no more a secret now. Read more about why AMD bought Xilinx and what is at stake.

AMD Buys Rival Xilinx For $35 B, Troubles Mount For Intel

By pocketing Xilinx, AMD becomes another hurdle for Intel along with NVIDIA. The integrated solutions these chip makers offer

Do you Really Need A GPU For Deep Learning?

Is acquiring a GPU an essential requirement for deep learning? Understanding GPU, its benefits, and exploring alternatives. In this article, we will understand what exactly a GPU and CUDA is, then explore the benefits of graphics processing units as well as when you should consider buying it if you are on a budget constraint.

Good-bye Big Data. Hello, Massive Data!

Join the Massive Data Revolution with Sqream. Shorten query times from days to hours or minutes, and speed up data preparation with - analyze the raw data directly.

Nvidia Warns Gamers of Severe GeForce Experience Flaws

Versions of Nvidia GeForce Experience for Windows prior to 3.20.5.70 are affected by a high-severity bug that could enable code execution, denial of service and more. The flaw specifically stems from the Nvidia Web Helper NodeJS Web Server.

Parallelizing GPU-intensive Workloads via Multi-Queue Operations

Achieving 2x+ speed improvements on GPU-intensive workloads by leveraging multi-queue operation parallelism using Vulkan Kompute. In this example we will show how we can achieve a 2x speed improvement on a synchronous example by simply submitting the workload across two queue families.

NVIDIA Smashes Performance Records On MLPerf Benchmarking

This has extended the company's lead on the industry’s only independent benchmark measuring AI performance of hardware, software & services.

A complete guide to AI accelerators for deep learning inference — GPUs, AWS Inferentia

A complete guide to AI accelerators for deep learning inference — GPUs, AWS Inferentia. Learn about CPUs, GPUs, AWS Inferentia, and Amazon Elastic Inference and how to choose the right AI accelerator for inference deployment

WSL2 / NVIDIA GPU Driver for DirectML and Tensor Flow

Configuring NVIDIA GPU for Windows Subsystem for Linux 2. I will explain how to install NVIDIA Driver on WSL2 (Microsoft Subsystem Linux) and test TensorFlow's parallel execution.

Installing CUDA and cuDNN on Windows

Installing CUDA and cuDNN on Windows. This is an how-to guide for someone who is trying to figure our, how to install CUDA and cuDNN on windows to be used with tensorflow.

The cost of “computational debt” in machine learning infrastructure

The cost of “computational debt” in machine learning infrastructure. How to maximize the utilization and scalability of your ML servers