Quadro GV100 GPU : Features, Specifications, Architecture, Working, Differences & Its Applications

Nvidia Quadro is a brand of graphics cards, used in workstations that run professional CAD (computer-aided design), CGI (computer-generated imagery), DCC (digital content creation) applications, machine learning, and scientific calculations. These graphics cards vary from the normal GeForce lines because the Quadro cards use ECC memory, better GPU cache, and improved floating point precision. Thus, the NVIDIA Quadro GV100 GPU was launched in 2018 at the GTC (GPU Technology Conference), which is an enthusiast-class professional graphics card. This article elaborates on the Quadro GV100 GPU, its working, and its applications.

What is the Quadro GV100 GPU?

The NVIDIA Quadro GV100 GPU is a high-end professional graphics card built on NVIDIA’s Volta architecture with a 12 nm process. It has features like a 32GB HBM2 memory, 320 texture mapping units, CUDA cores, 128 ROPs, 5120 shading units & 640 Tensor cores for deep learning acceleration and parallel processing. This GPU delivers significant memory capacity, scalability, and performance for tasks like ray tracing, deep learning, high-resolution visualization, VR, simulation, photorealistic rendering, AI, etc.

The GV100 GPU is a large chip with 815 mm² of die area & 21,100 million transistors. NVIDIA GPU has connected 32 GB HBM2 memory through the Quadro GV100 and connected with a 4096-bit memory interface. This graphics card can be operated at 1132 MHz frequency, boosted up to 1627 MHz, and the running memory is at 848 MHz. The NVIDIA Quadro GV100 GPU has a dual-slot card that draws power from an 8-pin power connector with a maximum power draw rated at 250 Watts.

How does the Quadro GV100 GPU Work?

The Quadro GV100 GPU works with thousands of CUDA cores (parallel processing units) to compute tasks using special Tensor cores dedicated to deep learning, AI research, high-end simulation & rendering. This GPU interacts with the host system above PCIe & NVLink high-bandwidth connections. In addition, it uses an advanced architecture with several computation, memory management & optimization layers.

This GPU with efficient HBM2 memory stores and transfers large datasets quickly. Tensor cores optimize deep learning workloads by achieving highly efficient matrix operations & leveraging FP16/FP32 mixed precision calculations. Thus, this GPU architecture allows for huge parallelization and speeds up graphics rendering, complex simulations & AI model training.

Step-by-step Working

The step-by-step working of the Quadro GV100 GPU follows as;

• First, the computer’s CPU sends commands & data like 3D models, matrices, or images to the GPU over a PCIe connection. So these commands can be for AI training, rendering, simulation, etc.
• The GPU divides the task into several small pieces that can be processed in parallel. So, it loads kernel programs, written in a parallel computing platform like CUDA, to run on its cores.
• The GV100 GPU includes CUDA Cores – 120 and Tensor Cores -640. Thus, CUDA cores handle general-purpose calculations and Tensor Cores for deep learning. These cores work in parallel, processing thousands of tasks simultaneously.
• Data can be stored within 32 GB of High Bandwidth Memory 2 (HBM2), which provides very quick access to large datasets. In addition, there are also caches and shared memory to decrease memory access time.
• After that, it performs specific calculations for graphics and AI/Deep learning. Thus, for graphics, it computes shading, lighting, textures & rendering. For AI/deep learning, Tensor Cores speed up training by executing quick matrix operations with FP16 or FP32 mixed precision. It handles simulations for scientific computing with high-precision FP64.
• If multiple GV100 GPUs are installed, then they can use NVLink to share memory/data much faster than PCIe. So this is important for large AI models & simulations. Once processing is done, the GPU sends results back to the CPU. In deep learning, it might go back to trained model weights or predictions. It renders the last image in graphics to the display.

Features

The features of the Quadro GV100 GPU include the following.

• It supports NVLink to connect two GV100 cards through 200 GB/s bandwidth.
• This GPU supports NVLink by connecting two GV100 cards for improved memory & performance.
• It supports up to four 5K monitors at 60Hz, dual 8K displays at 60Hz for each card & HDR color for 4K at 60Hz & 10/12b HEVC encode/decode.
• Its HDR color support delivers realistic and vibrant visuals.
• Its ECC memory ensures data integrity by detecting & correcting errors
• SLI Support allows multi-GPU configurations for improved performance.
• It is optimized for DCC, CAD & visualization applications.

Specifications

The specifications of the Quadro GV100 GPU include the following.

• It is built on Volta architecture with a 12 nm process.
• This GPU has 5120 CUDA Cores.
• Tensor Cores – 640.
• GPU memory is 32GB HBM2.
• Memory Bandwidth is 870 GB/s.
• Memory Interface is 4096-bit.
• FP64 Performance – 7.4 TFLOPS and FP16 Performance – 29.6 TFLOPS.
• FP32 Performance – 14.8 TFLOPS and INT8 Performance – 59.3 TOPS.
• System Interface is PCI Express 3.0 x16, and Tensor Performance is 118.5 TFLOPS.
• It includes four DisplayPort 1.4 connectors.
• Maximum power consumption is 250W.
• Form factor is Dual Slot, Full Height.

Quadro GV100 GPU Architecture

The Quadro GV100 GPU uses NVIDIA’s Volta architecture, designed for professional workstations, mainly for HPC, AI, graphics, VR, and rendering workloads. This architecture includes different components like CUDA cores – 5120, Tensor cores – 640, and HBM2 memory -32GB with a 4096-bit interface. In addition, the GV100 GPU can also support NVLink, which allows connections between two GPUs to increase performance and memory capacity. Each component of the Quadro GV100 GPU Architecture is discussed below.

CUDA Cores

The CUDA cores within the Quadro GV100 GPU are the basic processing units that allow parallel implementation of instructions for a variety of tasks. This GPU architecture has 5,120 CUDA cores, which are mainly designed for parallel processing. Thus, it is significant to speed up various workloads like HPC, graphics rendering, and AI.

Tensor Cores

The NVIDIA Quadro GV100 GPU uses 640 Tensor Cores, which speed up AI and deep learning workloads. These Tensor Cores execute matrix operations very efficiently by delivering major performance enhancements for deep learning training as compared to earlier GPU generations.

HBM2 Memory

The GV100 GPU has 32 GB of HBM2 (High Bandwidth Memory 2), which provides a high-bandwidth interface for quick data access & transfer. Thus, this HBM2 implementation provides up to 870 GB/s peak memory bandwidth, which is extensively faster than earlier Pascal-based Quadro cards. The HBM2 memory in this CPU can be built with a 4096-bit interface, which supports ECC (Error Correcting Code).

NVLink

The GV100 GPU supports NVLink technology, which allows high-speed data transmission & memory sharing between several GPUs, effectively creating a larger memory. Thus, it allows increased memory ability & performance scaling for applications, which allows GPUs to split memory. Thus, this GPU has two NVLink connectors, which support bidirectional bandwidth up to 200 GB/s while connecting two cards. So this surpasses the achievable speeds extensively with fixed PCI Express connections.

DisplayPort 1.4 Connectors

The Quadro GV100 GPU has four DisplayPort 1.4 connectors, which support different high-resolution displays & refresh rates like 5K at 60Hz and 4K at 120Hz. In addition, it can also support HDR over HDCP 2.2, DisplayPort 1.4 & a 12-bit inner display pipeline. Also, it can drive up to 4 monitors at the same time by providing NVIDIA Mosaic & Sync features for large-scale visualizations.

ECC Memory

The Quadro GV100 GPU is built on the NVIDIA Volta architecture with ECC (Error Correction Code) memory. Thus, the 32 GB of HBM2 memory in this GPU has ECC functionality, which helps in detecting and correcting memory errors, essential for professional applications that need high reliability.

Performance

The GV100 CPU delivers high performance in a variety of professional workloads like deep learning, graphics, simulation, and VR.

• The Tensor cores in deep learning speed up training & inference.
• 5120 CUDA cores & high memory bandwidth allow smooth rendering & high-resolution displays.
• The double-precision performance is compatible with complex simulations.
• The overall performance & memory capacity support challenging VR experiences.

Quadro GV100 GPU Software

NVIDIA Quadro GV100 GPU needs several software components for best performance and functionality, mainly its graphics driver. In addition, it supports a variety of NVIDIA software tools for professional workflows, like Unified Memory for proficient data management, CUDA for parallel computing & enterprise-management tools by NVIDIA for system management.

The Key Software Components mainly include the following.

• NVIDIA Quadro drivers are necessary for the GPU to work properly, which allows communication with the OS to provide optimized performance for specialized tasks.
• CUDA Toolkit is a parallel computing platform & programming model by NVIDIA that allows developers to use the processing power of GPUs for a wide range of applications like deep learning, high-performance computing, and AI.
• NVIDIA RTX desktop manager software improves multi-display productivity by supplying tools for optimizing & managing multiple monitors.
• NVIDIA Quadro Experience is a companion application that provides some features like streaming, screen recording & application organization to update professional workflows.
• NVIDIA AI Workbench tool shortens AI development on GPUs by providing access to tools, AI models, and blueprints for developers.

Quadro RTX Vs Quadro GV100 GPUs

The Quadro RTX series and Quadro GV100 are professional GPUs, but they can be different in their architecture, features & performance characteristics. Thus, the difference between Quadro RTX and Quadro GV100 GPUs includes the following.

Advantages

The advantages of the Quadro GV100 GPU include the following.

• The GV100 GPU has Volta architecture with 5120 CUDA cores, which delivers single-precision TFLOPS – 14.8, double-precision TFLOPS -7.9, and half-precision TFLOPS – 29.6. In addition, it includes Tensor Cores -640 by allowing TFLOPS – 118.5 with deep-learning performance.
• The GV100 GPU boasts HBM2 memory – 32GB with a 4096-bit memory bus, providing 870 GB/s of BW.
• Its tensor cores speed up deep learning training & inferencing.
• In addition, it supports superior VR features and has the memory ability to handle composite, immersive VR experiences.
• The GV100 allows real-time ray tracing, which delivers life-like lighting, reflections & shadows. So this is essential for design visualization & cinematic rendering applications.
• NVLink technology enables connecting various GV100 cards to further scale memory & performance for extremely challenging workloads.
• This GPU supports DisplayPort 1.4, which allows upto four 5K monitors at 60Hz, otherwise dual 8K displays for each card.
• NVIDIA Quadro Experience provides tools to shorten workflows, capture content, and control displays for collaboration & sharing.

Disadvantages

The disadvantages of the Quadro GV100 GPU include the following.

• The GV100 GPU is much more expensive than newer alternatives.
• These GPUs are Inefficient for mainstream use and gaming.
• It consumes up to 250 Watts of power, which is significantly higher than newer workstation GPUs.
• Its Volta architecture is older with first-generation tensor cores, thus it lacks enhancements in AI acceleration and efficiency provided by newer Ampere architectures.
• Virtual GPU setups frequently need NVIDIA GRID licensing, which can limit usage in addition to complexity and virtualized environments.

Applications

The applications of the Quadro GV100 GPU include the following.

• This GPU is ideal for large neural network Training (LLMs, CNNs, and transformers), GPU-accelerated data analytics like RAPIDS, Dask, & Batch inference within production.
• Up to 118.5 TFLOPS allows higher throughput and faster convergence than various consumer GPUs.
• It is well-suited for Weather modeling, fluid dynamics, Quantum chemistry simulations, Seismic analysis, Astrophysics, etc.
• It is frequently used in universities, enterprise research clusters, and national labs.
• This GPU manages large-scale finite element analysis & computational fluid dynamics simulations within apps; Abaqus, ANSYS, COMSOL Multiphysics, etc.
• It is used in studios for photorealistic rendering, real-time VR content creation, Large-scene rendering, etc.
• It is used with qualified VR developers for Training simulators, VR-based engineering reviews, medical VR applications, etc.
• In addition, this GPU supports Quadro vDWS (Quadro Virtual Data Center Workstation) for remote GPU access within virtualized environments.
• It is perfect for centralized GPU rendering or compute in enterprise/cloud setups.

Thus, this is an overview of a powerful Quadro GV100 graphics card, designed to handle the most demanding professional applications. So, this GPU provides outstanding double-precision performance to make it appropriate for scientific & technical computing applications that need accuracy. Here is a question for you: What is a GPU?