Breaking into the most compelling GPU announcements this year, the NVIDIA A100 GPU sets a new standard for data centres across the globe. Find out how.
Unveiling the All-New NVIDIA A100 Data Center GPU
In the wake of proliferating AI networks, NVIDIA introduced the new A100 Tensor Core GPU in May this year. Designed especially for HPC, AI, and data analytics, the A100 GPU promises significantly speedy performance beyond the prior NVIDIA Tesla V100 GPU. The A100 GPU is packed with exciting new features that are poised to take computing and AI applications by storm.
Built for modern data centers, NVIDIA A100 GPUs can amplify the scaling of GPU compute and deep learning applications running in-
- Single and Multi-GPU Workstations
- Servers and clusters
- Cloud data centers
- Edge computing systems, and
- Supercomputers
Touted as the ‘8th Generation Data Center GPU’, NVIDIA A100 claims to become a powerhouse for elastic, versatile, and high throughput data centers. Tensor Core GPU’s architecture is based on NVIDIA Ampere GPU coupled with new CUDA software advances.
Let’s take a closer look at the tech specifications of this brand new A100 GPU.
Technologies Powering NVIDIA A100 GPU
The announcement of the NVIDIA A100 Tensor Core GPU has rewritten history by delivering over 1.5 terabytes for frame buffer bandwidth. Below are some other unique features of this Ampere GPU-
1. 54 Billion Xtors
NVIDIA affirms that the A100 GPUs crams 54 billion transistors onto an 826 mm^2 size of a die. It is considerably larger than the earlier models, namely NVIDIA’s flagship gaming card, RTX 2080 Ti, and the V100. This incredible transistor density (7-nanometer processor) along with a huge die size translates to almost three times the speed of RTX 2080 Ti.
2. 3rd Gen Tensor Cores
Not only transistors but the A100 is also aiming high with 6,912 FP32 CUDA cores, 3,456 FP64 CUDA cores, and 422 Tensor cores. Together with the range of FP32 and the precision of FP16 results in no code change during model training.
Here’s the difference between the operating speed of the V100 FP32 matrix on the left and the A100 accelerated TF32 on the right.
3. Fine-grained Structured Sparsity
Another mega introduction in the A100 GPU is the fine-grained structured sparsity that doubles the compute throughput for deep neural networks. Considering the heavy sparsity of most neural networks, the approach zeros out smaller weights to retrain the network with 2X data processing.
Such an amplified processing speed is extremely beneficial for applications involving precision and physics simulations.
4. Multi-instance GPU
Talking about hardware, the new multi-instance GPU (MIG) feature enables the A100 to be easily partitioned into seven separate GPU instances. NVIDIA offers these 7 GPU instances as part of their DGX A100 system to be adjusted like a pod into a server. This hardware utilization makes DGX ideal for 56 different users with each one of them experiencing an equivalent performance of a Volta.
The pack becomes much more with 9 Mellanox ConnectX-6 200Gb/s network interface and 15TB Gen4NVME SSD.
5. 3rd Gen NVLink
Last but not the least, A100’s third-generation NVLink interconnect is a power booster for the GPU’s scalability and reliability. To support successful data transmission, NVLink provides link-level error detection and packet replay mechanism combined with its low-latency shared memory interconnect architecture.
When, Where, and How to Use NVIDIA A100 GPUs
Hardly four months after its release, the A100 has already become a star performer for tech giants.
Businesses worldwide are capitalizing on this remarkable collaboration of various cloud providers with NVIDIA A100 GPUs to build dynamic applications. Below are some effective use cases-
1. Data Analytics
The ongoing digital transformation of businesses has exploded data ingestion from disparate sources. Thanks to big data analytics, data scientists can now make sense of enormous data, both structured and unstructured.
The NVIDIA A100 propels data analytics by providing ready-to-run, optimized AI software. It eliminates time-consuming set-up with a simple plug-in start that encourages businesses to build high-performance AI models.
2. AI Model Training and Inference
Earlier, GPUs were confined to perform domain-specific tasks with either training or inference. With NVIDIA A100, companies get the best of both worlds with an accelerator for training as well as inference. This means that A100 GPUs can not only support AI model training but also helps to analyze new models and make predictions. Applications like predictive analytics for healthcare, demand forecasting, business sales, and a lot more are now seemingly possible with A100.
3. Deep Video Analytics
Another AI application emerging as an essential breakthrough overriding manual workload is deep video analytics. From media publishers to surveillance systems, deep video analytics is the new vogue for extracting actionable insights from streaming video clips. NVIDIA A100’s capacity to transmit 1.5 terabytes of data makes it perfect for image recognition, contactless attendance, and other deep learning applications.
Ultimately, the power-pack of NVIDIA A100 GPU awaits the tech industry to leverage it for innovating futuristic technology solutions.
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