Graphics processing units (GPUs) are important components of high-performance computers used in many advanced applications. Graphics cards’ parallel processing capabilities make them suitable for many advanced computing uses, including video rendering, supporting deep learning systems, and training machine learning algorithms. The ability to process multiple commands simultaneously supports accelerated computing in diverse applications like artificial intelligence, gaming, and robotics.

NVIDIA is the industry leader in GPU production, commanding over 80% of the graphics card market. Their processors regularly get high marks in reviews from reliable industry sources such as Tom’s Hardware. We are going to look at the technology that distinguishes NVIDIA GPUs and the architectures utilized in GPU production. We’ll also identify some of the company’s most popular GPU models for gaming, video processing, and business uses, such as training machine learning models.

Advantages of NVIDIA Graphics Processor Technology

The technology employed in manufacturing NVIDIA GPUs provides some advantages over the products of other GPU suppliers like AMD and Intel. These benefits may be important when selecting a GPU to address specific business, manufacturing, or data science usage scenarios.

CUDA Cores

Compute Unified Device Architecture (CUDA) is a proprietary parallel computing platform and application programming interface (API) model created by NVIDIA. CUDA cores are smaller and more efficient than CPU cores. They are designed to process multiple tasks simultaneously, providing support for applications leveraging parallel processing. The cores are integral components of NVIDIA graphics cards that increase the speed of applications by exploiting the capabilities of the GPU.

NVIDIA GPUs utilize thousands of CUDA cores to construct a massively parallel architecture capable of efficiently handling multiple tasks simultaneously. These specialized cores offer enhanced speed when processing large datasets and performing complex mathematical calculations. CUDA cores are instrumental in accelerating application performance in fields like big data analytics, machine learning, and other areas of artificial intelligence.

Tensor Cores

Tensor Cores are specialized processing units introduced by NVIDIA in 2017. The cores are designed to accelerate AI workloads by improving matrix math performance. Tensor cores can significantly enhance the parallel processing required by deep learning tasks. NVIDIA has advanced its Tensor Core technology with each generation of its GPUs.

The advantages of a GPU equipped with Tensor Cores include:

  • Exceptional performance that maintains accuracy while reducing AI training time;
  • Breakthrough inference performance with low latency, high throughput, and maximum GPU utilization;
  • High-performance computing (HPC) support for superior accuracy and speed to accelerate next-generation scientific research.

Real-Time Ray Tracing

NVIDIA’s GPUs provide real-time ray tracing that simulates light’s physical behavior. Ray tracing enables the creation of exceptionally realistic visuals for use in gaming, business, and scientific applications. This technology allows a developer to produce a more lifelike visualization than alternate video rendering solutions.

NVIDIA developers utilize deep learning supersampling (DLSS) to leverage the power of AI, providing improved image quality for better gaming performance. DLSS upscales lower-resolution images to higher resolutions for enhanced performance with no loss of visual quality. NVIDIA ray tracing provides enhanced rasterization performance for the 3D and 2D modeling critical to the entertainment and scientific communities.

Accelerated Computing

NVIDIA GPUs support accelerated computing with their massively parallel architecture. Multiple features of the GPUs support the demands of high-performance computing.

  • Energy efficiency is prioritized in GPU design to control energy costs in large data centers and HPC environments.
  • NVIDIA’s NVLink and Scalable Link Interface support scalability by enabling multiple GPUs to work together for enhanced computational power to support tasks like machine learning training and scientific simulations.

NVIDIA GPU Architectures

NVIDIA GPUs are manufactured utilizing several different architectures. An overview of these architectures illustrates their differences and the applications they are designed to address. New NVIDIA GPUS are constructed using the following architectures. Some NVIDIA GPUs using older architectures are still being sold and offer excellent performance.

Hopper Architecture (March 2022)

The Hopper architecture provides an accelerated computing platform to support demanding, next-generation workloads. The architecture securely scales diverse workloads in any size data center utilizing the following technological innovations.

  • The transformer engine optimizes floating-point operations to accelerate AI calculations for transformers and training models.
  • The NVLink, NVSwitch, and NVLink Switch System support GPU clusters and scalability to meet the requirements of HPC tasks and trillion-parameter AI models
  • NVIDIA Confidential Computing protects data and applications while in use to close gaps in traditional encryption methods.
  • The second-generation Multi-Instance GPU (MIG) feature enables a GPU to be partitioned into multiple smaller, fully isolated instances with dedicated memory, cache, and compute cores.
  • DPX instructions accelerate dynamic programming, an algorithmic technique for solving complex recursive problems by breaking them down into simpler subproblems.

Ada Lovelace Architecture (September 2022)

The Ada Lovelace architecture focuses on providing energy-efficient and enhanced GPU performance to power AI, graphics, video, and other demanding computing tasks. Following are some of the features powering this GPU architecture.

  • Third-generation RT Cores double ray tracing performance for photorealistic graphic rendering.
  • Fourth-generation Tensor Cores accelerate throughput to support transformative AI technologies like natural language processing (NLP), generative AI, and computer vision.
  • CUDA Cores double the processing speed of single-precision floating point (FP32) operations over previous GPU versions.
  • Advanced video and AI vision acceleration are supported by NVIDIA’s optimized AV1 stack to enhance performance in areas like video conferencing, augmented reality (AR), and virtual reality (VR).
  • Ada Lovelace GPUs are optimized for enterprise data center operations. They are tested and supported for maximum performance, durability, and security.

Blackwell Architecture (March 2024)

Blackwell is NVIDIA’s newest architecture and introduces groundbreaking advancements to support generative AI and accelerated computing. The following are some of the distinctive features of this advanced GPU architecture.

  • Blackwell-architecture GPUs are manufactured with 208 billion transistors utilizing a custom-built TSMC 4NP process. They offer exceptional speed for the most demanding AI applications.
  • The second-generation Transformer Engine accelerates large language model (LLM) and mixture-of-experts (MoE) model inference and training.
  • Secure AI is provided with NVIDIA Confidential Computing to protect sensitive and valuable data with strong hardware-based security.
  • NVLink and NVLink Switch support seamless communication between all GPUs in a server cluster.
  • The Decompression Engine provides a high-speed link to accelerate database queries for superior performance in data analytics and data science applications.
  • A dedicated Reliability, Availability, and Serviceability (RAS) Engine proactively identifies potential faults to minimize downtime and enhance resiliency.

Popular NVIDIA GPU Reviews

While not all-inclusive, the following list identifies some of the popular NVIDIA GPUs on the market. We’ll look at the chips’ features and settings where they are typically deployed.

NVIDIA GeForce GTX 16 Series

The GeForce GTX 16 series is designed to supercharge the gaming experience with powerful graphics performance. GeForce GTX 16 series GPUs are available as graphics cards for a PC or integrated into powerful laptops. The GPUs are an excellent choice for gamers.

  • Turing shaders provide improved performance and power efficiency for a faster, cooler, and quiet gaming experience.
  • Faster graphics deliver higher frame rates for smoother gameplay.
  • The inclusion of game-ready drivers ensures excellent speed and smooth performance with graphics-intensive games.
  • Advanced graphics and video rendering bring new levels of realism to virtual reality (VR) projects.

GeForce GTX 16 series GPUs are equipped with 512 to 1536 CUDA cores and either 4GB or 6GB of memory. They are built with NVIDIA’s legacy Turing architecture.

NVIDIA GeForce RTX 20 Series

GeForce RTX 20 series graphics cards and laptops provide powerful performance and cutting-edge features with dedicated ray tracing and AI cores. Gamers can maximize settings and video resolution for an enhanced visual experience.

The GeForce RTX 20 offers users low latency and excellent responsiveness for a competitive edge when playing their favorite games. The GPU delivers advanced streaming capabilities with the NVIDIA Encoder.

Creative endeavors are supported by the NVIDIA Studio platform, which features dedicated drivers and tools meant to unlock a user’s imagination. The NVIDIA Broadcast app lets you use an RTX 20 to transform any room into a home studio with powerful AI features like noise removal and virtual backgrounds.

GeForce RTX 20 series GPUs feature from 1920 to 4352 CUDA cores for superior parallel processing. Graphics cards have between 6GB and 12GB of memory. The chips are built utilizing the Turing architecture and come equipped with 1st generation ray tracing cores and 2nd generation Tensor cores for enhanced performance.

NVIDIA GeForce RTX 30 Series

The GPUs utilized in the GeForce RTX 30 series are designed to provide high performance to creators and gamers. The GPUs feature 2nd generation ray tracing cores and 3rd generation Tensor Cores for enhanced graphics performance and support for AI technologies.

The processors employ deep learning supersampling (DLSS) to boost speeds for a more immersive gaming experience. NVIDIA Reflex technology provides unparalleled responsiveness and exceptionally low latency. Competitive gamers can get an advantage from the technology built into the RTX series.

GeForce game-ready drivers have been finely tuned in collaboration with developers and tested extensively to provide users with maximum performance and reliability. Game settings can be optimized with a single click for a more enjoyable gaming session.

RTX 30 series GPUs are available with 2304 to 10752 CUDA cores. The processors are built with the Ampere architecture and offer memory from 6GB to 24GB to ensure fast performance for the most demanding graphics applications.

NVIDIA GeForce RTX 40 Series

The GeForce RTX 40 series are the most powerful NVIDIA GPUs designed for consumer use. They are manufactured utilizing the advanced Ada Lovelace architecture for supercharged performance. New streaming multiprocessors deliver up to double the performance of the RTX 30 series and offer improved power efficiency.

Revolutionary AI graphics are delivered with a combination of DLSS, third-gen ray tracing cores, and fourth-gen Tensor Cores. NVIDIA Reflex technology minimizes latency and provides higher frame rates. The RTX 40 series supports creativity with a suite of exclusive tools for video editing and graphic design.

Additional features of the GPU series include RTX Video Super Resolution which automatically enhances video played in your web browser. NVIDIA G-Sync offers high refresh rates for smooth gameplay. The graphics capabilities of the RTX provide the high performance required by virtual reality applications.

RTX 40 series GPUs are available with from 3072 to 18394 CUDA cores. Dedicated Shader Cores enhance graphics performance to deliver photorealistic presentation. On-board memory ranges from 8GB to 24GB to address the needs of complex calculations and applications.

NVIDIA L40S

The NVIDIA L40S offers unparalleled AI and graphics performance for enterprise data centers. The processor is designed to power next-generation data center workloads, including graphics rendering, LLM training and inference, and generative AI applications. The L40S supports the most demanding data center workloads by utilizing the Ada Lovelace architecture, fourth-gen Tensor Cores, and third-gen ray tracing cores.

The L40S is designed for efficiency and security. It is optimized for data center operations and extensively tested to ensure maximum performance, durability, and uptime. The GPU delivers breakthrough performance for generative AI and LLM inference applications. NVIDIA Confidential Computing ensures data security.

The L40S provides 48GB of GPU memory with a bandwidth of 864GB/s. It features 18,176 CUDA cores, 142 third-generation ray tracing cores, and 568 fourth-generation Tensor Cores. Its maximum power consumption is 350W, offering the performance and energy efficiency required for enterprise data center use.

NVIDIA H100 NVL

NVIDIA’s H100 Tensor Core GPU delivers exceptional performance, scalability, and security for every workload. The GPU leverages the technology of NVIDIA’s Hopper architecture to speed up the performance of LLMs by 30X. A dedicated Transformer Engine allows the GPU to solve trillion-parameter language models

The power available in the H100 increases performance while maintaining accuracy when working with LLMs. Utilizing NVLink and NVSwitch enables large GPU clusters to be deployed for accelerated data analytics and exascale high-performance computing. NVIDIA Confidential Computing ensures the security of data processed by the H100.

Atlantic Net’s NVIDIA GPU Solutions

Atlantic Net offers customers dedicated high-performance servers in two powerful configurations designed to meet your advanced computing needs.

  • Our NVIDIA L40S GPU server option is an excellent solution for general AI tasks, machine learning, and deep learning applications.
  • Our NVIDIA H100 NVL server is more powerful than the L40S, offering higher memory bandwidth for enhanced AI and deep learning requirements.

Contact us to learn more about how our hosted NVIDIA GPU server solutions can help your business thrive.