NVIDIA Tesla V100 : Volta Architecture, Features, Specifications, Working, Differences & Its Applications

The NVIDIA Tesla V100 was a revolutionary GPU,  officially released by NVIDIA in 2017 during the GTC (GPU Technology Conference). It was the primary GPU based on NVIDIA’s Volta architecture. This Tesla V100 had a huge impact on the AI field. Thus, it marks a breakthrough in accelerated computing for deep learning, AI, HPC, scientific simulations, and many more. The V100 has become a foundation of current AI infrastructure, primarily in cloud-based services, supercomputing, and data centers. Although it has been succeeded by other latest models like the A100, its legacy in the research and AI communities remains important. This article elaborates on NVIDIA Tesla V100, it’s working & its applications. It is one of the world most advanced data centre GPU IN 2017.

What is NVIDIA Tesla V100?

The NVIDIA Tesla V100 is a powerful and most advanced data center GPU designed to speed up artificial intelligence, HPC, graphics, and data science applications. It is designed with NVIDIA Volta architecture in both 16 GB and 32GB configurations with HBM2 (High Bandwidth Memory 2). Thus, it provides up to 100 CPU performance within a single GPU.

In addition, it helps to speed up training times, mainly for AI models & other data-intensive tasks. Thus, NVIDIA Tesla V100 is suitable if you are looking for the most adaptable Data Center GPU in your HPC and AI applications for outstanding performance.

Tesla V100 Features

The features of NVIDIA Tesla V100 include the following.

• The V100 GPU uses NVIDIA’s Volta architecture by integrating specialized hardware to boost HPC & AI performance significantly.
• The Tensor Cores speed up matrix operations for inference and deep learning training. Thus, provides extensive performance gains over earlier generations.
• It is equipped with HBM2 memory to provide very high memory bandwidth.
• The NVLink connects various V100 GPUs, which allows faster communication between them.
• The V100 GPU supports FP16 and FP32 mixed-precision operations for accelerating AI training while maintaining accuracy.
• The GPU includes a large number of CUDA cores for improving its capability for data science and HPC tasks.
• Its ECC memory ensures reliability and data integrity, which is essential for HPC and demanding data center environments.
• In addition, it supports virtualization by allowing for efficient GPU resource sharing across several users.

Tesla V100 Specifications

The specifications of NVIDIA Tesla V100 include the following.

• It uses NVIDIA Volta GPU Architecture.
• Memory size is 32 GB/16 GB HBM2.
• It includes 5,120 CUDA Cores and 640 Tensor Cores.
• Memory BW is 900 GB/sec.
• FP64 or double-precision performance is 7.8 TFLOPS.
• FP32 or single-precision performance is 15.7 TFLOPS.
• System interface is PCIe Gen3.
• Tensor performance is 125 TFLOPS.
• Its maximum power consumption is 300 Watts (SXM2) or 250 Watts (PCIe).
• It has NVLink interconnection with 300 GB/s bidirectional bandwidth.
• Form factor is PCIe Full Length/Height or SXM2.

NVIDIA Tesla V100 Architecture

The NVIDIA Tesla V100 is built on NVIDIA’s Volta architecture with a TSMC 12nm FFN manufacturing process. It is a high-performance process specially customized by TSMC for NVIDIA. It is a significant advancement from the earlier 16nm process, designed for accelerating AI & HPC workloads.

NVIDIA Tesla V100 Architecture Components

NVIDIA Tesla V100 Architecture includes significant components like Tensor Cores, CUDA cores, HBM2 memory, and many more. In addition, this V100 GPU can also have the NVLink interconnect, used for fast multi-GPU communication. It can also support mixed-precision computing to increase AI training performance.

CUDA Cores

CUDA Cores in Tesla V100 are the basic processing units used for general-purpose parallel processing. This GPU includes 5,120 CUDA cores, which handle a wide range of computing tasks. These CUDA cores provide significant acceleration with Tensor Cores for both AI and HPC workloads by allowing parallel processing of tasks.

Tensor Cores

The NVIDIA Tesla V100 includes 640 specialized Tensor Cores. It allows for quick model development & deployment for difficult AI applications. It is designed specially to speed up deep learning workloads by executing a high-throughput matrix multiply-accumulate operation within a single step. These specialized cores extensively enhance performance for deep learning training & inference.

High-Bandwidth Memory

The NVIDIA Tesla V100 GPU is equipped with 32 GB/16 GB HBM2 memory. Thus, it provides up to 900 GB/s of memory bandwidth for large-scale computations and rapid data processing.

This HBM2 provides significant speed benefits over earlier memory designs by allowing quick data access for complex models and large datasets. The V100 GPU was accessible in both 16GB & 32GB HBM2 configurations, including the stacked memory on the same package for better power & area efficiency.

NVLink Interconnect

This NVLink in the NVIDIA Tesla V100 GPU is a high-speed interconnect that communicates with various V100 GPUs. Thus, it allows efficient data transfer and scalable performance between GPUs within a system. This NVLink provides a low-latency and high-speed interconnect, which provides higher bandwidth, significantly up to 300 GB/s, as compared to fixed PCIe connections. This is essential for accelerating demanding HPC & AI workloads to attain faster complex deep learning models training & higher application performance.

Mixed-Precision Computing

The NVIDIA V100 supports mixed-precision computing by combining FP16 & FP32 calculations to speed up AI workloads while maintaining accuracy. The Volta architecture of the V100 GPU allows for quicker FP16 matrix operations while collecting results within FP32. Thus, it helps in maintaining model accuracy & training stability by offering a wider dynamic range by avoiding numerical underflow. Thus, this combination decreases memory usage, accelerates data transfers & improves overall compute performance, which leads to faster training times & the capability to instruct larger models.

NVIDIA Tesla V100 Software

NVIDIA Tesla V100 software includes the NVIDIA drivers, the NGC Catalog, AI enterprise, CUDA, cuDNN, Deep Learning Frameworks, and DGX Software, which are explained below. The key examples mainly include the AI Workbench of NVIDIA for simplified development, DGX software used for deep learning on DGX systems & support for TensorFlow& MXNet frameworks.

• NVIDIA drivers are a significant low-level software that allows the OS to communicate & use the V100 GPU for all HPC, graphics, and AI workloads.
• NVIDIA AI Enterprise is a complete software set designed mainly for data centers by providing tools for data management, GPU monitoring & AI development. The parallel computing platform & programming model of NVIDIA allows developers to control the power of the GPU for general-purpose programming.
• The cuDNN or CUDA deep neural network library is a deep neural network primitive library that speeds up the expansion of high-performance deep learning applications on GPUs.
• Libraries like PyTorch, TensorFlow, Caffe2, and MXNet are optimized to run on the V100 GPU and take advantage of its Tensor Cores mainly for deep learning workloads.
• DGX Software is a completely integrated software stack available in DGX systems including frameworks, tools, and libraries for AI, deep learning, etc.

How does NVIDIA Tesla V100 Work?

The NVIDIA Tesla V100 GPU works with its Volta architecture by combining specialized Tensor Cores, CUDA Cores, high-speed interconnect & high-speed memory for scaling. In addition, the V100 GPU can also scale performance by its high-bandwidth HBM2 memory by connecting several GPUs with NVLink. Thus, it allows complex models and datasets to be processed much faster than fixed CPUs.

Step-by-Step Working

The step-by-step working of the NVIDIA Tesla V100 GPU is discussed below.

• First, the data transfer can be done from the CPU to the global memory of the GPU for processing.
• After that, TensorFlow/PyTorch software sets up a task for the GPU to manage, like training a neural network or executing scientific calculations. This can be separated into smaller kernels, which can be executed by the GPU.
• The V100 GPU includes 5120 CUDA cores, which handle a small element of the task. Thus, these cores function in parallel, which means they can perform several operations simultaneously to accelerate computation.
• The specialized processing units, like Tensor Cores, are used for deep learning tasks such as matrix multiplications. Thus, they perform computations quickly as compared to CUDA cores with mixed-precision to enhance speed without sacrificing precision.
• Its Global Memory contains large datasets, whereas Shared Memory is utilized for quick access through cores. In addition, Registers in this architecture store short-term results for separate tasks by ensuring fast access in computations.
• The V100 GPU includes 80 streaming multiprocessors, where each SM contains numerous Tensor Cores and CUDA cores. These SMs help in dividing and processing tasks.
• NVLink allows various Tesla V100 GPUs to be connected to each other for extra power if required.
• Thus, it ensures that data can quickly flow between GPUs by allowing them to work on similar tasks as a group. When the task is finished, the results can be stored back within the Global Memory.
• The final results can be transferred back to the CPU from the GPU for further analysis. The V100 GPU is built with cooling to prevent overheating. Thus, this GPU adjusts its usage of power based on the workload to conserve energy.

Difference between Tesla V100 and Tesla P100

The difference between Tesla V100 and Tesla P100 GPUs includes the following.

Evolution of NVIDIA Data Center GPUs

Over the years, NVIDIA has continuously advanced its GPU lineup for AI and HPC. Below is the progression from Tesla P100 to the latest Blackwell B200.

NVIDIA GPU Timeline

• 2016 – Tesla P100 (Pascal architecture)
  • First GPU with HBM2 memory, designed for HPC.
• 2017 – Tesla V100 (Volta architecture)
  • Introduced Tensor Cores for AI acceleration.
• 2020 – A100 (Ampere architecture)
  • Huge leap in mixed precision and AI training.
• 2022 – H100 (Hopper architecture)
  • Transformer Engine, massive boost for LLMs.
• 2024 – B200 (Blackwell architecture)
  • Dual-chip GPU, optimized for trillion-parameter AI models.

Advantages

The advantages of NVIDIA Tesla V100 include the following.

• The V100 delivers major performance gains by providing up to 100 CPU cores within a single GPU.
• Its Tensor Cores significantly speed up deep learning performance for both inference & training.
• It handles large datasets & complex models within data science and scientific simulations without swapping extreme data.
• NVLink Interconnect allows high-speed and low-latency GPU-to-GPU communication. Thus, it is essential for scaling applications across various V100 GPUs for large-scale and complex simulations.
• The V100 GPU provides higher performance for each watt, which decreases required power consumption & cooling in data centers.
• It provides verified reliability for compute-intensive and demanding workloads to make it a solid choice for moderately sized simulations and existing infrastructure.
• The NVLink & Tensor Cores combination provides an extremely scalable platform for cloud services and data centers.
• It is available in 16GB & 32GB configurations, which accommodate the complex models and large datasets in modern AI & simulations.

Disadvantages

The disadvantages of NVIDIA Tesla V100 include the following.

• It provides less performance compared to A100.
• It is less future-proof as AI workloads progress.
• These are expensive and consume more power.
• It doesn’t have active cooling, thus it depends on the chassis to supply fans for the required airflow.

Applications

The NVIDIA Tesla V100 applications include the following.

• The V100 GPU severely decreases the required time to train complex AI models.
• It delivers high-throughput and fast inference, making it suitable for real-time applications such as image recognition, natural language processing (NLP), and recommendation systems.
• It speeds up demanding HPC tasks like seismic analysis, molecular dynamics, weather & climate modeling.
• In addition, this GPU is used for economic modeling & analysis by providing the required power for difficult computations.
• Researchers utilize this GPU to examine large datasets & run simulations necessary for determining new drugs.
• The unified architecture of V100’s GPU with Tensor and CUDA Cores allows it to excel at data analysis. Thus, it provides quick insights from big volumes of data.

Thus, this is an overview of NVIDIA Tesla V100, features, specifications, architecture, software, differences, advantages, disadvantages & its applications. It is the most superior data center GPU which  is powered by Volta architecture and available in both 16GB & 32GB configurations. It provides up to 100 CPU performance in a single GPU. Thus, it can speed up high-performance computing, AI, data science & graphics. Here is a question for you: What is Tesla P100?