NVIDIA A30 : Specifications, Architecture, Working, Differences & Its Applications

The NVIDIA A30 Tensor Core GPU was introduced as a mid-range data center GPU, launched on April 12, 2021, in the virtual GTC 2021 conference. This GPU was introduced next to the NVIDIA A10 as a versatile, mid-range accelerator. It is built on the Ampere architecture, designed to provide flexible compute acceleration primarily for mainstream enterprise servers, high-performance computing, and AI inference, as well as data analytics and training workloads. It balances power efficiency and performance for a wide range of workloads, including AI inference, training, data analytics, and high-performance computing (HPC). This article provides an in-depth look at the NVIDIA A30, its operation, and its applications.

What is NVIDIA A30?

The NVIDIA A30 is a versatile server GPU designed to accelerate various workloads like AI inference, high-performance computing (HPC), and data analytics in mainstream enterprise workloads. It can be built on the NVIDIA Ampere architecture and features 24GB of HBM2 memory with high memory bandwidth for processing large datasets efficiently. Thus, its key features include the ability to be partitioned using Multi-Instance GPU (MIG) technology for multi-tenant environments and high performance across different precisions (like FP64, TF32, and FP16).

Specifications

The specifications of NVIDIA A30 include the following.

• NVIDIA A30 is a versatile data center GPU.
• It uses NVIDIA Ampere architecture.
• Total board power is 165 W.
• The thermal solution is Passive.
• Mechanical form factor is FHFL (Full-height, full-length) 10.5, dual-slot.
• GPU Base clock is 930 MHz
• Boost clock is 1440 MHz
• PCI Express interface is PCI Express 4.0 ×16, Lane & polarity reversal supported.
• Memory clock is 1215 MHz.
• Memory type is HBM2.
• Memory size is 24 GB.
• Memory bus width is 3072 bits.
• Peak memory bandwidth is up to 933 GB/s.
• It includes CUDA Cores – 3804 and Tensor Cores – 224.
• System Interface is PCIe Gen 4.0 x16.
• Peak Single-Precision or FP32 is 10.3 TFLOPS.
• Peak Double-Precision (FP64) – 5.2 TFLOPS (10.3 TFLOPS with Tensor Core)
• Tensor Float 32 (TF32) Perf is 82 TFLOPS.
• Peak INT8 Performance is 330 TOPS or 661 TOPS with sparsity.
• Interconnect is Third-gen NVLink.

How Does NVIDIA A30 Work?

The NVIDIA A30 Tensor Core GPU works with its particular Ampere architecture components, like the Multi-Instance GPU or MIG and third-generation Tensor Cores. Thus, it speeds up a wide range of data center workloads like AI inference, training & HPC. The NVIDIA A30 accelerates many tasks by performing parallel calculations very efficiently, optimizing memory utilization, managing a variety of data types, permitting efficient resource sharing, etc. In addition, this GPU merges specialized hardware & software with efficient memory & interconnects to speed up data-intensive enterprise workloads.

NVIDIA A30 Architecture

The NVIDIA A30 GPU is built with the Ampere architecture, designed for different data center workloads like high-performance computing, AI inference, training, etc. This GPU includes HBM2 memory – 24GB, bandwidth – 933 GB/s, and MIG (Multi-Instance GPU) support. Thus, it allows it to be divided for instant workloads. This architecture allows high performance within different precisions like FP64 for HPC & a variety of Tensor Cores mainly for AI.

NVIDIA A30 Architecture Components

The NVIDIA A30 Tensor Core GPU architecture can be built with different components, designed for AI inference, high-performance computing, deep learning, and data analytics workloads.

Tensor Cores

The third-generation tensor cores are a significant feature of this architecture. It accelerates a complete range of mixed-precision math operations like TF32, FP64, BFLOAT16, INT8, INT4, and FP16. Thus, it provides significant performance gains for AI, HPC, and data analytics. In addition, they can also support structural sparsity to provide up to 2x higher performance, mainly for sparse AI models. They can also add almost 30% more FP64 performance, mostly for HPC workloads, reaching 10.3 TFLOPS.

CUDA Cores

The A30 GPU features CUDA cores – 3,584 in its Ampere architecture for general-purpose parallel processing tasks. The main function of these cores is to provide the parallel processing power for different workloads like data science, professional visualization, AI inference, and many more. In addition, it can also have 3rd Generation 112 Tensor Cores, which accelerate matrix operations & 28 RT Cores for ray tracing. These can be optimized for mixed-precision and single-precision (FP32) workloads.

Multi-Instance GPU or MIG

The MIG technology allows this GPU to be separated securely into up to four separate, hardware-isolated GPU instances. Each GPU instance contains its own dedicated memory, compute cores, and cache. Therefore, this enables IT administrators to deliver correctly sized GPU acceleration for various workloads & users by ensuring guaranteed QoS (Quality of Service) and increasing GPU utilization for various workloads.

HBM2 Memory

The A30 GPU is equipped with HBM2 memory – 24 GB, providing 933 GB/s of memory bandwidth. It is a type of 3D-stacked SDRAM (synchronous dynamic random-access memory where many memory layers are vertically stacked and interconnected by TSVs (through-silicon vias & a silicon interposer. Thus, it allows for a much wider memory bus as compared to traditional GDDR memory types.

The wide interface HBM2 architecture allows lower latency and high-speed data transmission. Thus, it is needed for compute-intensive workloads like data analytics, HPC, AI inference, & training. HBM2 is power-efficient as compared to fixed GDDR memory because of the adjacency of the memory to the GPU die & shorter data paths. Thus, it makes it compatible for data center environments, wherever power consumption is a main concern. The stacked and compact design of this permits high memory capacity in a smaller physical footprint on the PCB as compared to a planar GDDR layout.

NVLink

This GPU uses Third-Generation NVLink by connecting two GPUs to provide high-speed multi-GPU communication. In addition, it can also provide bidirectional peer-to-peer interconnect bandwidth up to 200 GB/s between two interconnected GPUs. Thus, it is helpful for large datasets & demanding HPC and AI workloads. The NVLink implementation in this GPU provides two times the throughput as compared to the earlier generation. Thus, it allows up to 330 TFLOPs combined deep learning performance whenever two cards are connected.

PCI Express Gen 4

This PCIe Gen 4 host interface in this architecture doubles the bandwidth as compared to Gen 3, enhancing data transfer speeds from the memory of the CPU. The NVIDIA A30 Tensor Core GPU features a PCI Express Gen 4.0 x 16 interface. This provides a total bidirectional 64 GB/s of bandwidth, which is double that of PCIe Gen 3.0. This bandwidth improves data transfer speeds between the CPU and GPU, which is particularly beneficial for data-intensive workloads common in AI, ML, and HPC applications.

Media Engines

The NVIDIA A30 GPU features media engines that accelerate video processing workloads, especially for decoding certain specialized tasks like optical tasks, decoding, JPEG processing, etc. There are different media engines included within the A30 GPU, like NVDEC (Four Video Decoders), NVJPEG (One JPEG Decoder), and OFA (Optical Flow Accelerator).

Power Efficiency

This is a power-efficient GPU that has 165W of maximum thermal design power. Thus, it is suitable in environments where cooling costs and energy consumption are a priority. Its efficiency can be achieved with its design and architecture focused on performance balance with lower power draw as compared to high-performance alternatives. It is ideal for cloud computing and data centers where operational costs are a main consideration.

Software Optimization

Software optimization of this GPU involves a group of system setups or a comprehensive software ecosystem, leveraging specific hardware features like Tensor Cores, and fine-tuning applications, particularly for AI, machine learning, and HPC workloads.

• The key software components of this system include drivers, core libraries and toolkits, operating systems, high-level software & frameworks, virtualization & orchestration.
• NVIDIA Drivers for the A30 GPU are used for operation.
• Core libraries and toolkits are a foundational software stack that depends mostly on the parallel computing platform of NVIDIA.
• This GPU is compatible with the main enterprise operating systems, like Linux and Windows.
• Therefore, this continuous software platform ensures scalability and versatility for a variety of enterprise workloads.

NVIDIA A30 Vs NVIDIA A100

Both the NVIDIA A30 and NVIDIA A100 GPUs are powerful data center accelerators. Thus, the difference between NVIDIA A30 and NVIDIA A100 includes the following.

How to Maintain the NVIDIA A30 GPU?

NVIDIA A30 GPU maintenance mainly involves ensuring the right environmental conditions, normal physical cleaning, and maintaining the software updated to ensure the best performance & longevity. Therefore, this is a data center GPU with a passive cooling design;, it depends on sufficient server airflow for the dissipation of heat. Physical cleaning regularly every 3 to 6 months, or more frequently in high-dust environments, is essential for heat dissipation. Software maintenance helps in monitoring the health of the GPU to ensure the best operation.

Advantages

The advantages of the NVIDIA A30 GPU include the following.

• It is a versatile GPU that manages a wide range of tasks.
• This GPU supports different mathematical precisions using its third-generation Tensor Cores, with TF32, BFLOAT16, FP64, FP16, INT4, and INT8 to make it flexible for different workloads.
• Its MIG feature partitions the GPU into minor and isolated GPU instances. Thus, it allows IT administrators to deliver right-sized acceleration for different jobs and users to optimize utilization & access.
• Its energy efficiency is low, which is helpful where power consumption is a concern, like in data centers.
• It delivers high performance for HPC and AI tasks.
• It’s 24GB of HBM2 memory with a 933 GB/s bandwidth, which allows managing large datasets & complex models efficiently.
• It features Third-gen NVLINK to provide 200GB/s of bandwidth for quick communication between GPUs within multi-GPU systems.
• The A30 GPU is a more cost-effective solution compared to higher-end GPUs for certain workloads.

Disadvantages

The disadvantages of the NVIDIA A30 GPU include the following.

• It is not as powerful as higher-end A100/H100 models.
• It has fewer Tensor cores and CUDA cores compared to other GPUs.
• Its HBM2/GDDR6 memory is not enough for managing very large datasets, complex simulations, or very large language models that need more memory.
• The A30 does not support complete NVLink connectivity for high-speed and seamless memory pooling across several GPUs.
• It is not designed for Display or Graphics.
• This GPU depends on server chassis airflow for cooling because it does not have fixed fans. Thus, it needs specific server configurations to ensure sufficient thermal management.
• In addition, the A30 GPU scalability and performance are limited compared to the finest data center accelerators.

Applications

The applications of the NVIDIA A30 GPU include the following.

• The A30 is optimized for deploying pre-trained AI models in production, enabling real-time applications such as NLP (Natural Language Processing), like Recommendation systems, computer vision, image analysis, automatic speech recognition, and many more.
• This GPU is suitable for miniature to medium-scale deep learning training, transfer learning, and fine-tuning for specific tasks.
• The GPU accelerates engineering & complex scientific simulations and calculations like CFD (Computational fluid dynamics), Physics simulations & weather forecasting, Genomics & molecular dynamics simulations, Energy modeling & financial analyses, etc.
• The A30 GPU analyzes and processes large datasets efficiently by accelerating different tasks like data processing, business intelligence, cleaning, etc.
• In addition, it can also be used in Media & Entertainment for professional visualization & media workflows like video editing, rendering, and hardware-accelerated video encoding/decoding.
• This GPU is highly optimized for deploying pre-trained AI models within real-time applications.

In summary, the NVIDIA A30 is a versatile server GPU built on the Ampere architecture, designed for enterprise and inference workloads. This GPU features third-generation Tensor Cores with 24 GB HBM2 memory & 933 GB/s bandwidth for high performance within HPC and deep learning. In addition, this GPU can also support MIG (Multi-Instance GPU) technology, which allows a single GPU to be separated into several, independent instances to exploit utilization by providing quality of service for different tasks. Here is a question for you: What is the NVIDIA A40 GPU?