Nvidia Tesla is the former name for a line of products developed by Nvidia targeted at stream processing or general-purpose graphics processing units (GPGPU), named after pioneering electrical engineer Nikola Tesla.

With 640 Tensor Cores, Tesla V100 is the world’s first GPU to break the 100 teraFLOPS (TFLOPS) barrier of deep learning performance. The next generation of NVIDIA NVLink™ connects multiple V100 GPUs at up to 300 GB/s to create …

Powered by NVIDIA Turing Tensor Cores, NVIDIA Tesla T4 provides revolutionary multi-precision inference performance to accelerate the diverse applications of modern AI

NVIDIA Tesla K80 is the world’s fastest HPC and machine learning accelerator, engineered to deliver superior application performance.

May 26, 2024 · Nvidia CEO Jensen Huang believes Tesla’s full self-driving (FSD) system is the most advanced system out right now. It also just so happens that Tesla’s FSD is powered by Nvidia's chips.

Nov 11, 2014 · You may already know NVIDIA Tesla as a line of GPU accelerator boards optimized for high-performance, general-purpose computing. They are used for parallel scientific, engineering, and technical computing, and they are designed for deployment in supercomputers, clusters, and workstations.

The NVIDIA Tesla V100 accelerator is the world’s highest performing parallel processor, designed to power the most computationally intensive HPC, AI, and graphics workloads. The GV100 GPU includes 21.1 billion transistors with a die size of 815 mm 2 .

NVIDIA’s new NVIDIA Tesla P100 accelerator (see Figure 1) using the groundbreaking new NVIDIA® Pascal™ GP100 GPU takes GPU computing to the next level. This paper details both the Tesla P100

Tesla is the codename for a GPU microarchitecture developed by Nvidia, and released in 2006, as the successor to Curie microarchitecture. It was named after the pioneering electrical engineer Nikola Tesla.

Jun 22, 2021 · ‘Incredible’ GPU cluster powers AI development for Autopilot and full self-driving. Tackling one of the largest computing challenges of this lifetime requires larger than life computing.