NVIDIA Ampere Architecture

The Heart of the World’s Highest-Performing, Elastic Data Centers

The Core of AI and HPC in the Modern Data Center

Scientists, researchers, and engineers are working to solve the world’s most important scientific, industrial, and big data challenges with AI and high-performance computing (HPC). Designers, engineers and creative professionals need to visualize increasingly complex content, creating cutting-edge products, telling immersive stories, or reimagining cities of the future. Meanwhile, enterprises are seeking to harness the power of AI to extract new insights from massive datasets, both on premises and in the cloud. The NVIDIA Ampere architecture, designed for the age of elastic computing, delivers the next giant leap by providing unmatched acceleration at every scale.

Groundbreaking Innovations

Crafted with 54 billion transistors, the NVIDIA Ampere architecture is the largest 7 nanometer (nm) chip ever built and features six key groundbreaking innovations.

Third-Generation Tensor Cores

First introduced in the NVIDIA Volta™ architecture, NVIDIA Tensor Core technology has brought dramatic speedups to AI, bringing down training times from weeks to hours and providing massive acceleration to inference. The NVIDIA Ampere architecture builds upon these innovations by bringing new precisions—Tensor Float 32 (TF32) and floating point 64 (FP64)—to accelerate and simplify AI adoption and extend the power of Tensor Cores to HPC.

TF32 works just like FP32 while delivering speedups of up to 20X for AI without requiring any code change. Using NVIDIA Automatic Mixed Precision, researchers can gain an additional 2X performance with automatic mixed precision and FP16 by adding just a couple of lines of code. And with support for bfloat16, INT8, and INT4, Tensor Cores in NVIDIA Ampere architecture Tensor Core GPUs create an incredibly versatile accelerator for both AI training and inference. Bringing the power of Tensor Cores to HPC, A100 and A30 GPUs also enable matrix operations in full, IEEE-certified, FP64 precision.

Third-Generation NVIDIA Tensor Core Technology
Multi-Instance GPU (MIG) Supports A100 & A30 NVIDIA GPUs

Multi-Instance GPU (MIG)

Every AI and HPC application can benefit from acceleration, but not every application needs the performance of a full GPU. Multi-Instance GPU (MIG) is a feature supported on A100  and A30 GPUs that allows workloads to share the GPU. With MIG, each GPU can be partitioned into multiple GPU instances, fully isolated and secured 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 across both bare-metal and virtualized environments.

Sparsity in AI Inference & Machine Learning

Structural Sparsity

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

Second-Generation RT Cores

The NVIDIA Ampere architecture’s second-generation RT Cores in the NVIDIA A40 and A10 GPUs deliver massive speedups for workloads like photorealistic rendering of movie content, architectural design evaluations, and virtual prototyping of product designs. RT Cores also speed up the rendering of ray-traced motion blur for faster results with greater visual accuracy and can simultaneously run ray tracing with either shading or denoising capabilities.

NVIDIA A100 Tensor Core GPU

Smarter and Faster Memory

A100 brings massive amounts of compute to data centers. To keep those compute engines fully utilized, it has a class-leading 2 terabytes per second (TB/sec) of memory bandwidth, more than double the previous generation. In addition, A100 has significantly more on-chip memory, including a 40 megabyte (MB) level 2 cache—7X larger than the previous generation—to maximize compute performance.

Converged Acceleration at the Edge

The combination of the NVIDIA Ampere architecture and the NVIDIA BlueField®-2 data processing unit (DPU) in NVIDIA converged accelerators brings unprecedented compute and network acceleration to process the massive amounts of data being generated in the data center and at the edge. BlueField-2 combines the power of the NVIDIA ConnectX®-6 Dx with programmable Arm cores and hardware offloads for software-defined storage, networking, security, and management workloads. With NVIDIA converged accelerators, customers can run data-intensive edge and data center workloads with maximum security and performance.

NVIDIA BlueField-2 A100

Density Optimized Design

 NVIDIA A16 GPU comes in a quad-GPU board design that’s optimized for user density and, combined with NVIDIA Virtual PC (vPC) software, enables graphics-rich virtual PCs accessible from anywhere. Deliver increased frame rate and lower end user latency versus CPU-only VDI with NVIDIA A16, resulting in more responsive applications and a user experience that’s indistinguishable from a native PC.

Inside the NVIDIA Ampere Architecture

Explore the cutting-edge technologies of the architecture and its full lineup of GPUs.