NVIDIA Tesla V100

NVIDIA Tesla V100
Name	NVIDIA Tesla V100
Manufacturer	NVIDIA
Family	Tesla
Released	2017
Architecture	Volta
Process	12 nm
Cores	5120 CUDA
Tensor cores	640
Memory	16 GB or 32 GB HBM2
Memory bandwidth	900 GB/s
Power	250–300 W

NVIDIA Tesla V100 The NVIDIA Tesla V100 is a server-class accelerator introduced in 2017 for high-performance computing, artificial intelligence, and scientific simulation. Designed on the Volta NVIDIA architecture, the product targeted data centers operated by Google, Amazon Web Services, Microsoft Azure, IBM, and research institutions such as Lawrence Berkeley National Laboratory and CERN. It served as a successor to prior accelerators used by projects like Human Genome Project compute deployments and large-scale initiatives at Oak Ridge National Laboratory and Argonne National Laboratory.

Overview

The Tesla V100 combined advancements from the Volta microarchitecture with high-bandwidth memory and dedicated matrix multiply units, positioning it for workloads in deep learning at organizations including OpenAI, DeepMind, Facebook AI Research, and Intel partner labs. NVIDIA marketed the V100 to enterprise customers such as Dell EMC, Hewlett Packard Enterprise, Lenovo, and cloud providers including Oracle Cloud and Alibaba Cloud. It became a building block for supercomputers like Summit (supercomputer) and influenced designs in exascale planning at the Department of Energy national laboratories.

Architecture and specifications

At the silicon level the V100 implemented the Volta GPU microarchitecture with 5120 CUDA cores and 640 specialized Tensor Cores for mixed-precision matrix operations, enabling acceleration for projects at Stanford University, MIT, UC Berkeley, and ETH Zurich. Fabricated on a 12 nm node by TSMC, it used 16 GB or 32 GB of HBM2 memory from suppliers such as SK Hynix and Micron Technology, delivering around 900 GB/s of memory bandwidth useful to workflows run by Netflix recommendation teams and genomics groups at Broad Institute. NVLink interconnect features allowed multi-GPU scaling in systems by Cray and chassis from Supermicro used in deployments at Lawrence Livermore National Laboratory.

Variants and form factors

The V100 was offered in multiple form factors: PCIe accelerator cards compatible with servers from Cisco Systems, SXM2 modules for high-density systems such as those used in NVIDIA DGX-1 appliances, and blades integrated into rack systems sold by HPE Apollo. OEMs produced V100-based solutions for academic centers like University of Cambridge computing clusters and private research at Toyota Research Institute. The range included 16 GB and 32 GB memory options, and configurations optimized for thermal envelopes in data centers operated by Equinix and hyperscalers like Tencent Cloud.

Performance and benchmarks

Benchmarks released by NVIDIA and independent labs demonstrated strong throughput on linear algebra kernels used by BLAS implementations and training benchmarks for models similar to those from AlexNet, ResNet-50, and transformer architectures used by Google Research and OpenAI. For mixed-precision workloads the Tensor Cores delivered up to a reported 125 TFLOPS in FP16 matrix multiply, while double-precision floating point (FP64) performance targeted work at Los Alamos National Laboratory and climate modeling groups such as those at NOAA. Performance scaled with NVLink in multi-GPU configurations in supercomputers like Summit (supercomputer), and comparisons were frequently drawn against accelerators from AMD and custom ASIC efforts like Google TPU.

Software and ecosystem support

The V100 integrated with NVIDIA software stacks including CUDA, cuDNN, TensorRT, and the NCCL library to support distributed training frameworks from TensorFlow, PyTorch, MXNet, and ecosystem tools used by enterprises such as SAP and Siemens. Research groups at institutions like Caltech and Imperial College London used the V100 with container platforms such as Docker and orchestration by Kubernetes on cloud offerings from Google Cloud Platform and Microsoft Azure. Vendor collaborations extended to partnerships with Red Hat and Canonical for optimized system images in HPC centers.

Market adoption and use cases

Adoption spanned deep learning research at Stanford AI Lab and industry labs at Baidu Research and Tencent AI Lab, to scientific simulation in astrophysics projects at NASA and computational chemistry groups at Pfizer and Roche. Enterprises applied V100 instances for recommendation systems at Amazon, fraud detection at JPMorgan Chase, and computational finance at firms like Goldman Sachs. In healthcare and genomics organizations including Broad Institute and Wellcome Sanger Institute the V100 accelerated variant calling and sequence alignment workflows. The card featured in AI-focused products such as NVIDIA DGX and influenced cloud offerings like the AWS P3 instance family.

Reception and legacy

The Tesla V100 received acclaim for advancing mixed-precision training and enabling breakthrough results reported by DeepMind and research teams at University of Toronto and Carnegie Mellon University. It set benchmarks that spurred competitors including AMD and inspired specialized accelerators such as Google TPU generations and custom silicon initiatives at Apple and Amazon. Over time the V100 was superseded by successors built on architectures like NVIDIA Ampere and NVIDIA Hopper, but it remains cited in academic papers from venues like NeurIPS, ICML, and SC Conference for contributions to scalable training and HPC deployments.

Category:Graphics processing units