For the past few years, NVIDIA has focused on providing groundbreaking graphics processing units (GPUs) to accentuate the user experience. To do this, NVIDIA has been offering various cards for varying needs, ranging from extreme gaming capabilities to advanced AI and machine learning tasks.
These GPUs quickly spread across every domain, from personal computers to data centers helping AI to grow exponentially.
However, at the edges of the network where small servers and IoT devices reside, data is still being sent to be processed elsewhere. This increases the server load and generates significant latency in the process.
The solution to these challenges is to move these processes closer to the data source, but the energy and space constraints at the edge present a roadblock in our path. This is where the NVIDIA A2 steps in.
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What is NVIDIA A2 GPU?
Nvidia A2 is a powerful GPU designed for accelerating cloud computing and virtualized workloads. It offers high-speed processing capabilities, enhanced virtualization support, and hardware-level security features, making it a top choice for cloud service providers and data centers.
NVIDIA’s AI-optimized A2 GPU is a well-balanced offering with a small form factor and low power design; this makes it particularly suitable for entry-level servers with space and power constraints used in industrial sites and for edge computing.
Now, let’s look at how NVIDIA A2 tensor core GPU is driving excitement in the AI and edge computing markets.
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Impressive AI performance across cloud, data center, and the edge
AI inference is still driving disruptive innovation in various fields, including internet services, healthcare and biosciences, banking, manufacturing, retail, and supercomputing. A2, along with the NVIDIA AI inference portfolio, allows AI applications to be deployed with fewer servers and much less power consumption, resulting in quicker insights at a reduced cost.
NVIDIA AI Enterprise, a complete cloud-native AI and data analysis software package. NVIDIA has used its years of expertise in AI and ML to curate this software package. This makes it possible to manage and scale AI and inference tasks spanning multiple clouds.
NVIDIA’s foundry partner Samsung’s 8-nanometer process is used to etch the A2. This is more suitable for hyper-scale and cloud data center workloads with modest machine learning inference workloads and edge computing workloads that require reasonable performance within constraints. The NVIDIA A2 is perfect for enterprise utilization.
How the A2 fares against modern CPUs and last gen GPUs at inference work loads AI inference is employed to enhance the lives of consumers by delivering intuitive, real-time experiences and extracting data from trillions of end-point cameras and sensors.
Edge and entry-level servers equipped with NVIDIA A2 Tensor Core GPUs outperform CPU-only servers by 20x, instantaneously enabling any server to handle modern AI.
The figure below compares the performance of the A2 and modern CPUs at AI inference workloads:
Throughout intelligent edge usage instances such as smart cities, manufacturing, and retail, NVIDIA A2-equipped servers deliver up to 1.3 times the performance. With a price-performance ratio of up to 1.6 times and a 10% boost in power efficiency over prior GPU generations (Tesla series), NVIDIA A2 GPUs handling Intelligent Video Analytics (IVA) applications offer highly efficient deployments.
The figure below depicts the increase in performance of the A2 over T4 at IVA applications:
The A2 operates at a 60W TDP (Thermal Power Design) and can be configured to run on as low as 40W. It does not need an external power supply since it is a single-slot card. This gives the A2 better thermal and power efficiency than the last-gen Tesla T4.
The figure below compares the difference in the TDP operating ranges of the A2 and T4:
Also Read: GPU for Faster Analytics Processing
NVIDIA A2 is paving the path to AI at the edge
The most striking trend in the market is the use of AI inference on edge devices that deliver better performance for smart objects and services. The A2 Tensor Core GPU contributes to the acceleration of AI computing power and smart services that will continue to benefit many business applications like data analytics, risk analysis, etc.
Let’s look at the technical specifications and performance metrics of A2 that back up its high adoption rate.
Third-Gen tensor cores
To provide excellent AI training and inference performance, the 3rd gen Tensor Cores in A2 enable integer math down to INT4(Integer 4) and floating-point math up to FP32(Float Point 32). Additionally, the NVIDIA Ampere architecture supports TF32(Tensor Float 32) and NVIDIA’s automated mixed precision (AMP) features.
It is vital to provide security at edge installations and network endpoints. For large-scale commercial operations, A2 allows secure communication, hardened rollback, and boot through trusted code authentication security that safeguards against harmful malware assaults.
Second-Gen ray tracing
A2 is equipped with specialized ray tracing RT Cores, allowing it to conduct rendering workloads at breakneck speed. It can simultaneously run ray tracing with either shading or de-noising capabilities, providing double the throughput over the prior generation GPUs.
Excellent performance in hardware transcoding
The exponential expansion of video applications necessitates real-time processing and the most recent hardware encoders and decoders. A2 GPUs entirely utilize dedicated hardware to speed up video encoding and decoding for the most popular video codecs, H.264, H.265, VP9, and AV1.
Also Read: Why GPU for Deep Learning
NVIDIA-powered cloud servers from Ace Cloud Hosting
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FAQs – NVIDIA A2 GPU
What is the Nvidia A2 GPU used for?
The Nvidia A2 GPU is designed for cloud computing and virtualized workloads, including delivering high-performance graphics and data-intensive applications to users.
What is the memory capacity of the Nvidia A2 GPU?
The Nvidia A2 GPU has 16GB of memory.
Does the Nvidia A2 GPU support virtualized graphics?
Yes, the Nvidia A2 GPU supports virtualized graphics and can be used with Nvidia vGPU software and Nvidia GRID.
Is the Nvidia A2 GPU designed for multiple concurrent users?
Yes, the Nvidia A2 GPU is designed to support multiple concurrent users and virtual desktops.
How does the Nvidia A2 GPU improve cloud computing environments?
The Nvidia A2 GPU provides high-speed processing capabilities, enhanced virtualization support, hardware-level security features, and support for multiple concurrent users, making it an efficient choice for cloud computing environments.
Does the Nvidia A2 GPU support hardware-level security features?
Yes, the Nvidia A2 GPU provides hardware-level security features to protect against potential security threats.
What is the power consumption of the Nvidia A2 GPU?
The power consumption of the Nvidia A2 GPU is not specified, but it is designed to be efficient for use in cloud computing environments. It is recommended to check with the manufacturer for detailed specifications.