The V100 will first appear inside Nvidia's bespoke compute servers. Eight of them will come packed inside the $150,000 (~£150,000) DGX-1 rack-mounted server, which ships in the third quarter of 2017.

Nvidia has taken the wraps off its newest accelerator aimed at deep learning, the Tesla V100. Developed at a cost of $3 billion, the V100 packs 21 billion transistors laid down with TSMC's 12 ...

NVIDIA's super-fast Tesla V100 rocks 16GB of HBM2 that has memory bandwidth of a truly next level 900GB/sec, up from the 547GB/sec available on the TITAN Xp, which costs $1200 in comparison.

Nvidia's Tesla V100 GPU equates to 100 CPUs. That means the speed limit is lifted for AI workloads.

NVIDIA Tesla V100 Specifications GPU: GV100 (Volta) CUDA cores: 5120 Transistors: 21.1 billion Node: 12nm SMs: 80 GPU Boost Clock: 1455MHz TFLOPs: 15 VRAM: HBM2 Memory Bandwidth: 900GB/sec Memory ...

The DGX-1V will arrive in Q3; those on a tighter budget may want to consider Nvidia's "personal AI supercomputer" - the DGX Station. It contains four Tesla V100 GPUs and costs $69,000.

According to Nvidia, the PCIe Tesla V100 accelerator should be available later this year and usual suspects, like HP, are likely to offer systems based on the Nvidia V100 accelerator.

Soon after unveiling the Tesla V100 data center GPU based on its next-generation Volta architecture at GTC 2017, NVIDIA CEO Jen Hsun Huang announced an updated, Volta-infused DGX-1 server ...

The V100 GPUs remain the most powerful chips in Nvidia’s lineup for high-performance computing. They have been around for quite a while now and Google is actually a bit late to the game here.

The new NVIDIA Tesla V100 PCI-Express HPC Accelerator is based on the advanced 12 nm “GV100” silicon. The GPU is a multi-chip module with a silicon substrate and four HBM2 memory stacks.

In this video from ISC 2018, Marc Hamilton from NVIDIA describes how the company is working with Dell EMC to accelerate AI and HPC workloads. “Dell EMC Deep Learning Ready Bundle customers include the ...