NVIDIA A40 GPU : Features, Specifications, Architecture, Working, Differences & Its Applications

NVIDIA launched a high-performance data center GPU on October 5, 2020. It utilizes the NVIDIA Ampere architecture, built on the GA102 graphics processor, with 48 GB of GDDR6 memory. Thus, it provides a powerful solution for AI-accelerated workflows, rendering, and virtual workstations. It is a data center GPU, designed mainly for AI, high-performance computing, and visual computing workloads. Thus, it provides high performance for demanding workloads like engineering, content creation, and design. This article elaborates on the NVIDIA A40 GPU, its working, and its applications.

What is the NVIDIA A40 GPU?

The NVIDIA A40 is a versatile GPU, built on the NVIDIA Ampere architecture, which enhances its abilities to manage the different workloads efficiently, making it a powerful tool mainly for professionals. It is designed for a variety of high-performance computing (HPC) and data center visual computing tasks. In addition, it can also be designed to tackle demanding workloads like data science, AI acceleration, simulation, virtual production, and 3D design.

The NVIDIA A40 architecture is designed with GPCs (Graphics Processing Clusters), TPCs (Texture Processing Clusters), SMs (Streaming Multiprocessors), ROPS (Raster Operators) & memory controllers. So the complete A40 GPU includes GPCs – 7, SMs -84, and TPCs – 42. The GPC in NVIDIA GPU architecture is the primary structural unit that includes all necessary graphics processing elements. It is responsible for a major portion of graphics & computing processing.

Every graphics processing cluster has a dedicated raster engine with various texture processing clusters, where each TPC has two SMs (Streaming Multiprocessors). In addition, each TPC includes a PolyMorph Engine that manages vertex processing tasks like geometry shading and tessellation.

Features

The features of the NVIDIA A40 GPU include the following.

• This GPU uses NVIDIA Ampere architecture.
• Its memory provides sufficient space for complex computations and large datasets.
• Memory BW allows for quick data transfer.
• PCIe Gen4 supports better data transfer speeds.
• It is designed for virtualized environments by supporting a variety of NVIDIA vGPU software solutions.
• It has three DisplayPort 1.4a connectors for high-resolution displays and multi-monitor setups.
• It is available with a passive thermal solution, appropriate for different data center environments.
• In addition, this GPU supports NVLink to connect various A40 cards to enhance memory capacity and performance.
• It provides very powerful AI acceleration capabilities, including its Tensor Cores, appropriate for AI workloads and deep learning.
• The RT Cores of this GPU allow real-time ray tracing for realistic visuals within supported applications.
• The A40 GPU prioritizes power efficiency by providing up to 2x superior performance than the earlier generation.
• It supports both single & double-precision floating-point operations.

Specifications

The specifications of the NVIDIA A40 GPU include the following.

• NVIDIA A40 GPU Architecture is NVIDIA Ampere
• It includes GPCs-7, SMs-84, TPCs-42, CUDA Cores / SM – 128.
• It includes 10752 – CUDA Cores / GPU, 4 – Tensor Cores / SM, 336 (3rd Gen) – Tensor Cores / GPU.
• This GPU has 84 (2nd Gen) – RT Cores.
• GPU Boost Clock is 1740 MHz.
• Peak FP32 TFLOPS – 37.4.
• Peak FP16 TFLOPS – 18.7.
• Peak INT8 TOPS – 299.8.
• Peak INT4 TOPS – 599.7.
• Peak FP16 Tensor TFLOPS – 149.7/299.4.
• Peak FP32 Tensor TFLOPS – 74.8/149.6.
• Peak INT4 Tensor TOPS – 599.7/1199.4.
• Peak INT8 Tensor TOPS – 299.8/599.6.
• Frame Buffer Memory Size & Type is 49152 MB GDDR6.
• Memory Interface is 384-bit.
• Memory Bandwidth is 696 GB/sec.
• Memory Clock is 14.5 Gbps.
• ROPs – 112.
• Pixel Fill-rate – 194.9 Gigapixels/sec.
• Texture Fill-rate – 334.6 Gigatexels/sec.
• L1 Data Cache/Shared Memory – 10752 KB.
• Texture Units – 336.
• L2 Cache Size is 6144 KB.
• Register File Size is 21504 KB.
• TGP (Total Graphics Power) is 300 W.
• Transistor Count is 28.3 Billion.
• Die Size is 628.4 mm².
• The manufacturing process is a Custom 8 nm Process.

NVIDIA A40 GPU Architecture

The NVIDIA A40 GPU uses the NVIDIA Ampere architecture, which provides a powerful arrangement of high-performance Tensor Cores, CUDA Cores, and RT Cores along with GDDR6 memory – 48 GB. So this architecture allows the A40 GPU to speed up demanding visual computing workloads within data centers. Thus, delivers high-performance ray tracing, professional graphics rendering, and AI-accelerated computing.

NVIDIA A40 GPU Architecture Components

This A40 GPU Architecture includes different components, which are explained below.

CUDA Cores

The A40 GPU includes 10,752 CUDA Cores, which provide the base for general-purpose parallel processing tasks like high-performance computing and graphics rendering. In addition, it can also have 84 RT Cores and 336 Tensor Cores.

Tensor Cores

The NVIDIA A40 GPU includes 336 third-generation Tensor Cores, which are specialized hardware units. These are mainly designed to speed up high-performance computing and AI workloads. They allow mixed-precision arithmetic, particularly matrix multiplication & accumulation operations, which are essential to deep learning. Thus, this results in significant performance speedups, mainly in tasks like inference and AI training without sacrificing precision.

RT Cores

This GPU includes second-generation specialized hardware units like RT Cores – 84, which deliver better ray tracing performance for practical visual effects & simulations. These are designed to speed up ray tracing calculations by enhancing the ray-traced rendering performance, particularly in applications like film production, virtual prototyping, and 3D design. In addition, RT cores in the NVIDIA A40 GPU provide up to 2x the throughput as compared to the earlier generation.

Memory

The A40 GPU can be equipped with GDDR6 memory – 48 GB with ECC (error-correcting code) for data reliability and integrity, which is important for data center applications. The 696 GB/s high memory bandwidth ensures quick data access & efficient transmission between the memory and GPU, which reduces bottlenecks. The 384-bit memory interface in this GPU provides a wide path for data to be supplied between the memory and the GPU.

Interconnects

The NVIDIA A40 GPU includes different interconnects like NVLink and PCIe Gen4, which are explained below.

The A40 GPU supports NVLink, which allows low-latency and high-speed communication between several GPUs, mostly helpful for scaling performance within multi-GPU systems. The PCIe Gen4 offers a high-bandwidth connection between the host system and GPU, which facilitates quick data transfer mainly for demanding workloads.

DisplayPort 1.4a

The A40 GPU has three DisplayPort 1.4a connectors used for driving high-resolution displays by supporting some features like Display Stream Compression and HDR for 8K displays. This DisplayPort drives up to two 8K displays at 60Hz and four 5K displays at 60Hz for each card. In addition, it supports DSC 1.2 display stream compression, which allows driving an 8K display above a single cable.

Power & Cooling

The A40 GPU by NVIDIA is designed for power efficiency with 300W maximum board power, which uses a passively cooled design that requires a thermal solution. Thus, it is passively cooled, which means it depends on external cooling and data center airflow solutions mainly for heat dissipation. In addition, it can also be configured for blower-style and active cooling.

NVENC

The NVIDIA A40 has a dedicated video encode & decode engine like NVDEC & NVENC, which support a variety of codecs like H.265 (HEVC), AV1, and H.264 (AVCHD). Thus, these components offload video processing tasks from the CPU by allowing very efficient handling of various video streams for different applications like virtual workstations, content creation, streaming, etc.

NVIDIA Encoder or NVENC’s main function is to handle video encoding, which converts raw video data into different compressed formats like H.265, H.264, AV1, and HEVC.

The main benefits of NVENC mainly include: supports a variety of codecs & resolutions like 4K & 8K, provides high encoding quality as compared to software encoders, offloads encoding tasks, etc.

NVDEC

The NVIDIA Decoder, or NVDEC, main function is to handling video decoding by changing compressed video data back into an observable format. The main benefits of this decoder include: it allows efficient decoding of various streams, supports a broad range of codecs like VC-1, MPEG-2, H.264, VP8, H.265, AV1, and VP9. This decoder accelerates video decoding for a variety of applications.

NVIDIA A40 GPU Software

NVIDIA designed the A40 GPU with virtual GPU (vGPU) software, enabling GPU virtualization for virtual desktops and applications. This capability allows the A40 GPU to excel in tasks such as graphics rendering, professional visualization, and virtual workstation workloads. Furthermore, this GPU pairs seamlessly with a range of NVIDIA software tools, optimizing its features and performance.

The A40 GPU is a significant component in the virtual GPU platform of NVIDIA, which is certified mainly for professional visualization. This software allows many virtual machines or users to share a single GPU by allowing virtual workstations & applications. This GPU supports different vGPU software solutions like RTX Virtual Workstation, Virtual Compute Server, and vPC/vApps. So this software expands the authority of the GPU to virtual environments, improving flexibility and performance.

How does the NVIDIA A40 GPU Work?

The NVIDIA A40 is a high-performance data center accelerator GPU that uses the NVIDIA Ampere architecture to bring significant performance developments over earlier generations, mainly in areas like deep learning, scientific simulations, and ray tracing. The breakdown of the NVIDIA A40 GPU Working follows as;

The A40 GPU is equipped with a huge number of CUDA Cores, which are the basic processing units in the GPU. Thus, these cores can be optimized for parallel processing by allowing the A40 GPU to manage computationally intensive tasks very efficiently. The CUDA cores are designed specifically for ray tracing, which is a method used to make reflections and realistic lighting in graphics.

Tensor Cores can be optimized for deep learning workloads by providing significant performance gains within areas like AI inference & training. So the Tensor Cores of A40 provide up to 5x the training throughput of the earlier generation. The A40 GPU boasts a large frame buffer of high-speed 48 GB – GDDR6 memory, allowing it to manage complex models and large datasets efficiently.

The A40 GPU uses the PCIe Gen 4 interface that doubles the bandwidth as compared to PCIe Gen 3, resulting in quick data transfer speeds between the GPU & the CPU of the system. Thus, this GPU is designed for mainstream servers, including a dual-slot form factor & enhanced power efficiency as compared to earlier generations.

NVIDIA A40 Vs NVIDIA A30

The difference between NVIDIA A40 and NVIDIA A30 includes the following.

Advantages

The advantages of the NVIDIA A40 GPU include the following.

• The A40 GPU has better ray tracing performance, which allows more realistic and faster rendering in CAD & simulation applications.
• The third-generation tensor cores speed up AI workloads like deep learning & ML tasks, which leads to quicker training & inference times.
• Its large memory capacity handles complex models and large datasets in large-scale simulations & data analysis tasks.
• MIG support allows a single GPU to be divided into many virtual GPUs, which allows efficient resource sharing & use in cloud environments.
• In addition, the A40 GPU maintains quite low power consumption, where energy efficiency is a main concern.
• The A40 GPU uses ECC (Error Correction Code) memory to detect & correct memory errors by ensuring data integrity, especially significant for critical applications.
• The A40 is flexible and appropriate for a variety of workloads like AI & deep learning, Data science, Rendering, visualization, and Virtual workstations.

Disadvantages

The disadvantages of the NVIDIA A40 GPU include the following.

• It has fewer CUDA Cores; thus, it falls behind the A6000 ADA in raw compute performance.
• The A40 GPU uses GDDR6 in place of GDDR6X, which results in lower memory bandwidth.
• The A40 lacks NVLink, which limits multi-GPU scalability.
• It has high power consumption, which potentially increases operational costs within large-scale deployments.

Applications

The applications of the NVIDIA A40 GPU include the following.

• Engineers designed this high-performance data center GPU to handle a wide array of demanding professional workloads, including machine learning, AI, virtual workstations, and high-end visualization applications.
• Its versatility makes it ideal for various tasks, such as computer vision and natural language processing.
• Additionally, the A40 GPU powers cloud gaming services, delivering high-quality streaming experiences.
• Its key strengths handle both compute-heavy and graphics-intensive tasks in various industries.
• In addition, the A40 GPU excels in both inference and deep learning training tasks through its third-generation Tensor Cores, accelerating AI workloads.
• The A40 GPU helps large-scale data processing & analytics by allowing faster data preprocessing & model experimentation.
• In addition, it allows multi-user GPU acceleration for cloud workstations by providing powerful workstations and virtual desktops for resource-intensive projects.
• It delivers high-quality 3D rendering in 3D, CAD design, and virtual production.
• It is appropriate for simulation and scientific computing workloads like molecular dynamics, climate modeling, and computational fluid dynamics.
• Its power allows for the making of immersive VR experiences, mainly in marketing and gaming.
• This GPU speeds up transcoding, video editing & high-resolution video processing.
• In addition, the ECC memory of the A40 GPU ensures consistent results to make it valuable for drug discovery, medical imaging & other research applications that need large amounts of data processing.

Thus, this is an overview of the NVIDIA A40 GPU, which is an evolutionary leap in multi-workload and performance capabilities. It combines best-in-class professional graphics with powerful AI acceleration and compute to meet today’s creative, scientific, and design challenges. In addition, the GPU brings high-tech features for simulation, ray-traced rendering, and virtual production to professionals anywhere and anytime. This drives the next generation of server-based workloads and virtual workstations. Here is a question for you: What is the NVIDIA A30 GPU?