To meet a future where every facet of our lives is augmented and intertwined with artificial intelligence (AI), we need a four times more powerful solution than today’s best accelerators.
NVIDIA provides the industry’s fastest, most robust, advanced computational platform for AI applications.
It has seized the opportunity to use its GPUs to accelerate artificial intelligence and machine learning. NVIDIA has also introduced one of the most powerful and efficient accelerators ever made – the NVIDIA A100 GPU.
A100 accelerators are built to power the next generation of supercomputers, AI, high-performance computing (HPC), and hyper-scale data centers. It delivers 3X energy efficiency, providing 20X faster performance, and offers about twice the bandwidth of the Volta card generation. It is also known as NVIDIA’s immediate replacement for the Volta-based V100.
The new A100 enables users to get the most out of their software investments while bringing reliable performance and efficiency to demanding workloads. It also assists in creating, training, and deploying neural networks more quickly than ever before.
But the biggest question surrounding the A100 is how much it competes against its closest rivals in the segment. Does its hype support reality, though?
Let’s find out.
Cut Through the Confusion: What Is NVIDIA A100?
NVIDIA, the leading company developing hardware and software technologies for AI, is elevating its game in a big way by introducing new GPU products that are crucial to powering AI systems.
The GPUs provide the processing capacity to accelerate various complex and unpredictable workloads, from scaling scientific computing, AI training, and inference applications to enabling real-time conversational AI.
NVIDIA’s new Tensor Core A100 GPU is here, finally. The NVIDIA A100 is the most powerful computer accelerator in the IT industry.
NVIDIA’s comparable performance comparison for all its GPU variants for the past four years demonstrates that the new A100 has 11X greater performance for HPC jobs.
11X more HPC Performance in Four Years via NVIDIA
So, what is it all about with the NVIDIA A100?
The A100 helps you unlock the full potential of deep learning frameworks with the following high-end features:
- With TSMC’s 7-nanometer and NVIDIA’s Ampere architecture, it has a double-slot PCI Express card and third-generation tensor cores that allow researchers to shorten the lengthy week-long training times of models to just a few hours.
- With Multi-Instance GPU (MIG), one A100 GPU can be partitioned into seven different GPU instances, each being completely hardware isolated and with its own high-bandwidth memory, processing cores, and cache. This helps enterprises leverage the full capacity of their elastic data centers while dynamically adapting to varying workload requirements.
- For uninterruptible real-time data processing and to achieve quick implementation of massive datasets, the A100 offers the world’s rapid GPU memory bandwidth at 2 TB/s (terabytes per second).
Also Read: Cloud GPUs: The Cornerstone of Modern AI
The Key Features of A100
The NVIDIA A100 acts as a component of a broader NVIDIA solution that enables businesses to create an extensive machine learning infrastructure. Its key attributes are as follows:
Multi-Instance GPU (MIG) Technology
MIG powers up the performance of the GPU hardware and simultaneously provides specified QoS and isolation between multiple clients, viz., virtual machines, processes, and containers.
With MIG, developers can access ground-breaking acceleration for all of their applications, and IT managers can provide the appropriate GPU acceleration for every task, maximizing usage and extending access to every user and application.
For instance, a user can create two MIG instances with 30 GB memory each, three instances with 20 GB, or even five instances of 10 GB, depending upon the size of the workloads.
There are millions of parameters in AI networks, such as complexity, optimization, etc. For accurate forecasts, not all of these parameters are required, and some of them can be set to zero or non-existent.
For sparse models, the A100’s Tensor Cores can deliver up to two times the performance. The sparsity characteristic can enhance model training even though AI inference benefits from it more readily.
High Bandwidth Memory (HBM2e)
The A100 can accentuate the absolute performance of all significant neural networking frameworks by delivering 1.7x more memory bandwidth than the previous generation.
By taking advantage of the power of up to 80 GB of high-bandwidth memory, it enables seamless deployments of extensive models that would not fit in the memory of other systems.
Third-Generation NV Link and NV Switch
The throughput of NVLink (Wire-based based communication protocol) in the NVIDIA A100 is two times greater than that of the predecessor generation.
When used in conjunction with NVSwitch (On-node switch design), up to 16 A100 GPU units can be connected at up to 600 gigabytes per sec (GB/s), enabling the fastest application performance on an individual server.
The new NVLink offers significantly increased GPU-GPU communication bandwidth, stronger error detection and recovery functions, and additional links per GPU and switch.
Third-Generation Tensor Cores
NVIDIA A100 provides a remarkable functioning of deep learning by delivering 312 teraFLOPS in performance.
As compared to NVIDIA Volta GPUs, A100 offers 20 times the Tensor floating-point operations/s (FLOPS) for training deep learning models and 20 times Tensor tera operations/s (TOPS) for deep learning inference.
Also Read: How To Find The Best GPU For Deep Learning
Employ NVIDIA A100 to Fast Track Deep Learning and Data Analytics
The complexity of AI models is surging as they amplify cutting-edge innovations like conversational AI. It takes a lot of computing power and scalability to train these models.
Deep learning and data analytics make use of leading-edge technologies such as GPUs to enhance their functionality and provide optimal results in the shortest amount of time.
Moreover, with no code modifications and an additional 2X improvement with automated mixed precision and FP16, NVIDIA A100 Tensor Cores with Tensor Float (TF32) offer up to 20X higher performance than the NVIDIA Volta.
This enables GPUs to perform multiple, simultaneous computations and accumulate many cores that use fewer resources without sacrificing efficiency or power.
According to NVIDIA, the A100 performs up to three times better on large AI training models.
Up to 3X Higher AI Training on Largest Models via NVIDIA
Data scientists must be able to analyze, visualize, and extract insights from large datasets. However, datasets dispersed across numerous servers frequently hinder scale-out solutions.
These workloads can be handled by accelerated servers and are supported by A100, which provides up to 8X enhanced implementation as compared to its predecessors (V100).
8X Faster than V100 on Big Data Analytics Benchmark via NVIDIA
Also Read: The New Wave of Cloud GPUs
The Most Powerful End-to-End Solution for Multiple Use Cases
The A100 is a component of the comprehensive NVIDIA deep learning solution, which includes building blocks for hardware, networking, software, libraries, and applications, as well as optimized AI models.
It enables researchers to generate practical results and scale up solution deployment into production, making it the most potent end-to-end AI and HPC solution for data centers.
Here are some use cases demonstrating the efficiency of A100.
AI Model Development and Inference
Domain-specific tasks involving either development or inference are highly complex and can be optimized by utilizing GPUs. You can execute both tasks by leveraging NVIDIA A100 as the accelerator and experience the best of both worlds.
When compared to prior GPUs, the new A100 speeds up development and inference by 3X to 7X.
Being able to achieve high end-to-end throughput is one of the fundamental issues in maintaining video decoding performance at a pace with development and inference performance on a DL platform.
This was fixed with the A100 GPU, which includes five NVDEC units as compared to the preceding GPU cards.
Researchers can now execute double-precision simulations that would typically take NVIDIA V100 ten hours to complete in just four hours due to the inclusion of double-precision Tensor Cores in A100.
High-Performance applications can take advantage of the A100 Tensor Cores’ TF32 precision to deliver up to ten times faster performance for single-precision dense matrix multiplication.
Natural Language Processing
As the demand for natural language processing has increased, more powerful hardware and new software tools have been developed to handle large amounts of data and complex tasks.
The A100 card is NVIDIA’s flagship NLP product which can scale a one trillion parameter model in a reasonable amount of time thanks to the scaling functions built into its architecture.
The A100 outperforms the previous generation of GPUs and delivers a significant performance boost over CPUs.
Augmented Fault and Error Detection
The latest generation of A100 GPUs has been designed to detect and identify faults faster, more reliably, and more efficiently than any previous generation, thanks to its Ampere architecture that utilizes a new error/fault identification.
The A100 Tensor Core GPU is built on a purpose-built architecture to maximize the engine’s focused-on-functionality, fault and error detection, isolation, and containment.
This ensures that as applications perform, data objects are always appropriately instantiated and that system faults can be quickly isolated.
Enterprise Ready GPUs with Ace
Ace offers high-end NVIDIA A100 GPUs that are powerful and energy-efficient to provide an advanced computing platform for data centers, HPC, at a reasonable price.
Our IaaS platform uses cutting-edge technology like OpenStack and Ceph to help companies build high-performing and secure data centers.
Ace IOPS instances can be instantly deployed to the selected data centers, allowing users to immediately begin using the cloud with DDoS protection.
Demand-driven provisioning of spinning servers up and down can be quickly accessed with NVMe drives, AMD Premium 64-bit CPUs, and AMD EPYC.
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