NVIDIA A100 GPUs on Cloud

NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration and flexibility to power the world’s highest-performing elastic data centers for AI,data analytics, and HPC applications. As the engine of the NVIDIA data center platform, A100 provides up to 20X higher performance over V100 GPUs and can efficiently scale up to thousands of GPUs, or be partitioned into seven isolated GPU instances to accelerate workloads of all sizes.


Elastic Data centers NVIDIA Ampere Architectural Innovations

Third-Generation NVIDIA Tensor Core: Performance and Versatility for HPC and AI

First introduced in the NVIDIA Volta™ architecture, NVIDIA Tensor Core technology has brought dramatic speedups to AI training and inference operations, bringing down training times from weeks to hours and providing massive acceleration to inference. The NVIDIA Ampere architecture builds upon these innovations by providing up to 20X higher FLOPS for AI. It does so by improving the performance of existing precisions and bringing new precisions—TF32, INT8, and FP64—that accelerate and simplify AI adoption and extend the power of NVIDIA Tensor Cores to HPC.

New TF32 for AI: 20X Higher Performance, Zero Code Change

As AI networks and datasets continue to expand exponentially, their computing appetite is similarly growing. Lower precision math has brought huge performance speedups, but they’ve historically required some code changes. A100 brings a new precision, TF32, which works just like FP32 while providing 20X higher FLOPS for AI without requiring any code change. And NVIDIA’s automatic mixed precision feature enables a further 2X boost to performance with just one additional line of code using FP16 precision. A100 Tensor Cores also include support for bfloat16, INT8, and INT4 precision, making A100 an incredibly versatile accelerator for both AI training and inference.

Double-Precision Tensor Cores: The Biggest Milestone Since FP64 for HPC

A100 brings the power of Tensor Cores to HPC, providing the biggest milestone since the introduction of double-precision GPU computing for HPC. The third generation of Tensor Cores in A100 enables matrix operations in full, IEEE-compliant, FP64 precision. Through enhancements in NVIDIA CUDA-X math libraries, a range of HPC applications that need double precision math can now see a boost of up to 2.5X in performance and efficiency compared to prior generations of GPUs.

Multi-Instance GPU: Seven Accelerators in One GPU

Every AI and HPC application can benefit from acceleration, but not every application needs the performance of a full A100. With Multi-Instance GPU (MIG), each A100 can be partitioned into as many as seven GPU instances, fully isolated at the hardware level with their own high bandwidth memory, cache, and compute cores. Now, developers can access breakthrough acceleration for all their applications, big and small, and get guaranteed quality of service. And IT administrators can offer right-sized GPU acceleration for optimal utilization and expand access to every user and application. MIG is available across both bare metal and virtualized environments and is supported by NVIDIA Container Runtime which supports all major runtimes such as LXC, Docker, CRI-O, Containerd, Podman, and Singularity. Each MIG instance is a new GPU type in Kubernetes and will be available across all Kubernetes distributions such as Red Hat OpenShift, VMware Project Pacific, and others on-premises and on public clouds via NVIDIA Device Plugin for Kubernetes. Administrators can also benefit from hypervisor-based virtualization, including KVM based hypervisors such as Red Hat RHEL/RHV, and VMware ESXi, on MIG instances through NVIDIA vComputeServer.


Third-Generation NVLink: Creates a SuperGPU with NVIDIA HGX A100

Scaling applications across multiple GPUs requires extremely fast movement of data. The third generation of NVLink in A100 doubles the GPU to GPU direct bandwidth to 600 gigabytes per second (GB/s), almost 10X higher than PCIe Gen4. Third-generation NVLink is in HGX A100 baseboards, available in four-GPU and eight-GPU configurations with NVSwitch in NVIDIA DGX™ A100; and servers from other leading computer makers.

Structural Sparsity: 2X Higher Performance for AI

Modern AI networks are big, having millions and in some cases billions of parameters. Not all of these parameters are needed for accurate predictions, and some can be converted to zeros to make the models “sparse” without compromising accuracy. Tensor Cores in A100 can provide up to 2X higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also improve the performance of model training.

Smarter, Faster Memory: Massive Bandwidth Driving Compute Efficiency

A100 is bringing massive amounts of compute to data centers. To fully utilize that compute performance, A100 has a class-leading 1.6 terabytes per second (TB/sec) of memory bandwidth, a greater than 70 percent increase over the previous generation. In addition to 40 gigabytes (GB) of HBM2 memory, A100 has significantly more on-chip memory, including a 40 megabyte (MB) level 2 cache, which is nearly 7X larger than the previous generation. This provides the right combination of extreme bandwidth on-chip cache and large on-package high-bandwidth memory to accelerate the most computationally intense AI models.


Deep Learning Training

AI models are exploding in complexity as they take on next-level challenges such as conversational AI. Training them requires massive compute power and scalability.

NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20X higher performance over the NVIDIA Volta with zero code changes and an additional 2X boost with automatic mixed precision and FP16. When combined with NVIDIA® NVLink®, NVIDIA NVSwitch™, PCI Gen4, NVIDIA® InfiniBand®, and the NVIDIA Magnum IO™ SDK, it’s possible to scale to thousands of A100 GPUs.

Deep Learning Inference

A100 introduces groundbreaking features to optimize inference workloads. It accelerates a full range of precision, from FP32 to INT4. Multi-Instance GPU (MIG) technology lets multiple networks operate simultaneously on a single A100 for optimal utilization of compute resources. And structural sparsity support delivers up to 2X more performance on top of A100’s other inference performance gains.

High-Performance Computing

To unlock next-generation discoveries, scientists look to simulations to better understand the world around us.

NVIDIA A100 introduces double precision Tensor Cores to deliver the biggest leap in HPC performance since the introduction of GPUs. Combined with 80GB of the fastest GPU memory, researchers can reduce a 10-hour, double-precision simulation to under four hours on A100. HPC applications can also leverage TF32 to achieve up to 11X higher throughput for single-precision, dense matrix-multiply operations.

High-Performance Data Analytics

Data scientists need to be able to analyze, visualize, and turn massive datasets into insights. But scale-out solutions are often bogged down by datasets scattered across multiple servers.

Accelerated servers with A100 provide the needed compute power—along with massive memory, over 2 TB/sec of memory bandwidth, and scalability with NVIDIA® NVLink® and NVSwitch™, —to tackle these workloads. Combined with InfiniBand, NVIDIA Magnum IO™ and the RAPIDS™ suite of open-source libraries, including the RAPIDS Accelerator for Apache Spark for GPU-accelerated data analytics, the NVIDIA data center platform accelerates these huge workloads at unprecedented levels of performance and efficiency.

References and Suggested Further Readings :-

  1. A100-Whitepaper | NVIDIA A100 Tensor Core GPU Architecture

  2. A100-Product | NVIDIA A100 Tensor Core GPU

  3. Datasheet:-NVIDIA-A100 | Tensor Core GPU

A100 GPUs On E2E Cloud

Now that you’ve already started to think of ways how you could utilize Ampere A100 GPUs, why not go with the best-suited cloud-based GPUs to maximize the performance and minimize the total cost of ownership?

Here’s why you should consider using E2E Networks’ Ampere A100 GPU Cloud:

  • All the GPU servers of E2E networks run in Indian datacenters, hence reducing latency.

  • Powerful hardware deployed along with cutting-edge engineering that renders increased reliability.

  • Uptime SLAs so that you worry less and do more

  • Inexpensive pricing plans designed according to the needs of customers.

These features not only make E2E GPU Cloud services stand out from others in the market but it also helps you to stay ahead of your competition by outperforming them.

Get started, pick and choose your best GPU plan.