NVIDIA has long dominated the GPU market with leading innovations and continuously pushing the GPU technology development forward. Growing into a multibillion-dollar industry, GPU markets have expanded from the graphics and gaming sector to AI (artificial intelligence), ML (machine learning), and DL (deep learning) solutions. These various types of applications require GPUs due to their massive number of cores that can simultaneously perform complex calculations that may be hard for the CPU to execute. Hence, GPUs are performance accelerators that offload the CPU’s workload for faster and more efficient processing.
What is an Industrial PC with NVIDIA GPU?
Industrial PC with NVIDIA GPU combines the ultra-rugged PC architecture with GPU’s powerful capabilities in running complex algorithms at the edge. The GPU capability for computing complex matrix, vectors, etc. in parallel makes GPU a great performance accelerator for intelligent edge computing solutions that involves AI, ML, and DL. However, industrial applications often come with extreme environmental challenges where standard desktop computers will easily fail. Especially for mission-critical applications, reliability, durability, and longevity are a priority to mitigate downtime risks amid deployments. Industrial PC with NVIDIA GPU is a robust computer with power GPU accelerators for sophisticated solutions such as real-time machine inferencing.
Why GPUs are Great for Deep Learning and Machine Learning Applications
Unlike today’s CPUs that come equipped with 2-16 cores, GPUs consist of thousands of cores, making them capable of calculating a big pile of linear algebra such as matrix multiplication and addition which are commonly used in AI models with a single instruction, multiple data (SIMD) operations that are well suited for GPUs. That said, various applications really can really benefit from robust GPU solutions.
Driving Factors for Incredible Industrial PC with NVIDIA GPU solutions
The ability to endure harsh environments while running complex solutions are achieved by industrial PC with NVIDIA GPU thanks to their various robust features and rugged component. All of the internal and external components are carefully chosen to meet the industrial standards including resistors, capacitors, PCBs, power choke, enclosures, and I/Os. That said, these are some of the main factors of a truly rugged industrial PC equipped with an NVIDIA GPU including extreme temperature range, tough shockproof, constant vibration resistance, power management protection, and more.
Compared to standard desktop PCs or workstations, industrial PCs with NVIDIA GPUs have rich I/Os that support legacy technologies that are often found around industrial applications.
Moreover, industrial computers are tested and validated in a reliable lab to ensure their reliability, durability, and longevity features.
Types of NVIDIA GPUs
NVIDIA offers a wide array of GPUs for different customer segments, we simply break it down into commercial and enterprise GPUs. There are several differentiators to classify each GPU group from types of cores, processor microarchitectures (GPU architecture), GPU editions, and the built functions. NVIDIA’s GeForce, Quadro, and Tesla GPUs each have a different focus. GeForce GPUs mostly focused on general use, gaming, or machine learning purposes. Quadro GPUs are focused more on enterprise workstations and 3D graphic designs. Lastly, Tesla GPUs focused on AI, deep learning, and machine learning applications with graphical computation and supercomputers.
Commercial GPU | NVIDIA GeForce
NVIDIA GeForce GTX is the GeForce 16 series that initially was introduced back in 2008. GTX stands for Giga Texel Shader eXtreme. The GTX series utilizes Turing architectures for their microchip design and uses Cuda cores that are capable of quickly calculate multiple matrix calculations in parallel. In the early days, the GTX series are popular in processing graphics-intensive applications like games and graphics engines. Afterward, machine learning models are utilizing thousands of Cuda cores for running their CNN (convolutional neural network) algorithm.
RTX stands for Ray Tracing Texel eXtreme, is a higher-end version of GTX with more additional features added. RTX also uses Turing microarchitecture with thousands of Cuda cores. Further, they are equipped with Tensor cores for machine learning applications that are much faster and efficient than the Cuda cores. In short, instead of executing multiprocessing calculations, Tensor cores can perform multiple multiprocessing calculations simultaneously at once. RTX GPUs are capable of executing real-time ray tracing for complex graphics. Ray tracing basically calculates all the light angles in video games while in action, which will show a much more realistic reflection, refraction, textures, and materials of its surroundings in real-time. Thanks to the RT cores of RTX GPUs that are specifically designed for ray tracing.
Enterprise GPUs | Tesla & Quadro
NVIDIA Quadro RTX series is a much more powerful GPU than the GeForce RTX series. Underlying on the same Turing microarchitecture design, Quadro GPUs are made for professional users for workstations, engineering simulations, 3D animators, and many more with much intense graphic processing demands. Quadro GPUs are for enterprises that run computer-aided design (CAD), computer-aided engineering (CAE), computer-generated imagery (CGO), digital content creation (DCC), scientific calculations, and machine learning applications. Quadro has much bigger memory bandwidth, memory size, higher TDP, faster clock speed, and much secure than GeForce RTX GPUs. The USB type C ports on the Quadro GPUs can be disabled to prevent data theft.
NVIDIA Tesla GPU is an AI supercomputer in a box. It’s a GPU specially designed as an AI accelerator for deep learning applications. The Tesla GPUs target the enterprise market by offering high-performance computing (HPC) capabilities targeting companies with AI, ML, and DL applications. The Tesla V100 GPUs have 640 Tensor cores making them the most advanced data center GPU ever built to accelerate AI, HPC, and graphics applications. Tesla V100 is the world’s first GPU that breaks the 100 teraflops barrier on deep learning performance, thanks to the hundreds of tensor cores and high memory bandwidth.