Nvidia unveils new GPU, designed for data centers and AI

guest · May 14, 2020

Nvidia unveils monstrous A100 AI chip with 54 billion transistors and 5 petaflops of performance
May 14, 2020
https://venturebeat.com/2020/05/14/...n-transistors-and-5-petaflops-of-performance/

Nvidia unwrapped its Nvidia A100 artificial intelligence chip today, and CEO Jensen Huang called it the ultimate instrument for advancing AI.
Huang said it can make supercomputing tasks — which are vital in the fight against COVID-19 — much more cost-efficient and powerful than today’s more expensive systems.

The chip has a monstrous 54 billion transistors (the on-off switches that are the building blocks of all things electronic), and it can execute 5 petaflops of performance, or about 20 times more than the previous-generation chip Volta. Huang made the announcement during his keynote at the Nvidia GTC event, which was digital this year.

The DGX A100 is the third generation of Nvidia’s AI DGX platform, and Huang said it essentially puts the capabilities of an entire datacenter into a single rack.

“You get all of the overhead of additional memory, CPUs, and power supplies of 56 servers … collapsed into one,” Huang said. “The economic value proposition is really off the charts, and that’s the thing that is really exciting.”
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guest · Jun 10, 2020

AMD reveals more details of Nvidia's DGX A100
June 9, 2020
https://www.techspot.com/news/85546-amd-reveals-more-details-nvidia-dgx-a100.html

In brief: AMD and Nvidia have been tech rivals for a long time, so this might come as a surprise: Team Red has just revealed more specifications of Nvidia’s Ampere-based DGX A100 AI system.

Nvidia revealed its Ampere GPU architecture last month in the form of its A100 GPUs. Based on the 7nm process and designed for cloud computing, AI, and scientific number crunching, it boasts 54 billion transistors, 6,912 CUDA cores, and 20 times the speed of the previous Volta solution.
Because of the partnership, Nvidia has allowed AMD to share further details of the DGX A100 system.

“Only 2nd Gen AMD EPYC processors can provide up to 64 cores and 128 lanes of PCIe 4 interconnectivity in a single x86 data center processor, and we’re excited to see how the power of the Nvidia DGX A100 system enables the I/O bandwidth to be effectively doubled,” said Raghu Nambiar, AMD’s corporate vice president of data center ecosystems and application engineering.
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As to why Nvidia chose its rival’s CPUs instead of Intel’s, whose Xeon chips were the preferred processors in the first DGX systems, the company said the extra features and performance offered by Epyc processors made it an easy choice.
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guest · Jun 22, 2020

Nvidia reveals PCIe 4.0 version of its Ampere A100 GPU
It's not for consumers
June 22, 2020
https://www.techspot.com/news/85718-nvidia-reveals-pcie-40-version-ampere-a100-gpu.html

What just happened? Nvidia has revealed the first PCI-Express variant of the Ampere A100 GPU that it unveiled last month, but gamers shouldn’t get excited: it’s designed to accelerate development in the fields of AI, data science, and supercomputing.

Nvidia finally showed off its new 7nm Ampere architecture last month in the form of the A100 GPU. Now, the company has announced a number of A100 powered systems from leading server manufacturers, including Asus, Dell, Cisco, Lenovo, and more.
Nvidia has also revealed a PCI-Express variant of the A100.

It has the same 6912 CUDA cores, 54 billion transistors, six HBM2 memory stacks, and 40GB of memory as the SXM4 version, but the new connectivity allows for easy installation into existing systems.
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As noted by ComputerBase (via Tom’s Hardware), there are some drawbacks to switching to PCIe 4.0. The TDP in this variant drops from 400W to 250W, which reportedly means a 10 to 50 percent performance penalty based on the workload, with the GPU more suited to short bursts rather than sustained loads.
There’s expected to be 30 A100-powered server systems available this summer, and over 20 more by the end of the year.

“Adoption of NVIDIA A100 GPUs into leading server manufacturers’ offerings is outpacing anything we’ve previously seen,” said Ian Buck, VP and GM of Accelerated Computing at Nvidia.
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guest · Jul 31, 2020

NVIDIA’s Ampere A100 GPU Is Unstoppable, Breaks 16 AI Performance Records, Up To 4.2x Faster Than Volta V100
July 30, 2020
https://wccftech.com/nvidia-ampere-a100-fastest-ai-gpu-up-to-4-times-faster-than-volta-v100/

NVIDIA has just posted the first real performance numbers of its Ampere A100 GPU and the results are insane. The company has broken a total of 16 performance records in AI-specific benchmarks & also beaten its main competitors in the specific machine learning performance category by a huge lead.

The results come in from MLPerf which is an industry benchmarking group formed back in 2018 with a focus purely on Machine Learning performance. The benchmark suite consists of a total of eight tests and NVIDIA has posted records in all with record training speeds.
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guest · Nov 18, 2020

NVIDIA Announces A100 80GB: Ampere Gets HBM2E Memory Upgrade
November 16, 2020
https://www.anandtech.com/show/16250/nvidia-announces-a100-80gb-ampere-gets-hbm2e-memory-upgrade

Kicking off a very virtual version of the SC20 supercomputing show, NVIDIA this morning is announcing a new version of their flagship A100 accelerator.

Barely launched 6 months ago, NVIDIA is preparing to release an updated version of the GPU-based accelerator with 80 gigabytes of HBM2e memory, doubling the capacity of the initial version of the accelerator. And as an added kick, NVIDIA is dialing up the memory clockspeeds as well, bringing the 80GB version of the A100 to 3.2Gbps/pin, or just over 2TB/second of memory bandwidth in total.
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The 80GB version of the A100 will continue to be sold alongside the 40GB version – which NVIDIA is now calling the A100 40GB – and it is being primarily aimed at customers with supersized AI data sets. Which at face value may sound a bit obvious, but with deep learning workloads in particular, memory capacity can be a strongly bounding factor when working with particularly large datasets.
So an accelerator that’s large enough to keep an entire model in local memory can potentially be significantly faster than one that has to frequently go off-chip to swap data.
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NVIDIA: NVIDIA Doubles Down: Announces A100 80GB GPU, Supercharging World’s Most Powerful GPU for AI Supercomputing

SC20—NVIDIA today unveiled the NVIDIA® A100 80GB GPU — the latest innovation powering the NVIDIA HGX™ AI supercomputing platform — with twice the memory of its predecessor, providing researchers and engineers unprecedented speed and performance to unlock the next wave of AI and scientific breakthroughs.

The new A100 with HBM2e technology doubles the A100 40GB GPU’s high-bandwidth memory to 80GB and delivers over 2 terabytes per second of memory bandwidth. This allows data to be fed quickly to A100, the world’s fastest data center GPU, enabling researchers to accelerate their applications even faster and take on even larger models and datasets.
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