NVIDIA A100 Tensor Core GPU @ Affordable Indian Price

The A100 introduces a significant upgrade with its third-generation Tensor Core, surpassing the performance of the V100 while providing extensive support for deep learning (DL) and high-performance computing (HPC) data types. Alongside this enhancement, it also introduces a new Sparsity feature that further doubles the throughput.

The A100 introduces the new TensorFloat-32 (TF32) Tensor Core operations, offering a seamless way to accelerate FP32 input/output data in deep learning (DL) frameworks and high-performance computing (HPC) workloads. These operations run 10 times faster than the V100 FP32 FMA (fused multiply-add) operations, and when combined with sparsity, they achieve a staggering 20 times faster performance.

The A100 introduces powerful Bfloat16 (BF16)/FP32 mixed-precision Tensor Core operations, delivering performance on par with FP16/FP32 mixed-precision computations. This enables seamless acceleration of deep learning (DL) workloads while maintaining accuracy. Additionally, Tensor Core acceleration extends to INT8, INT4, and binary data types, providing comprehensive support for DL inferencing. Notably, the A100’s sparse INT8 operations outperform V100 INT8 by a remarkable 20 times.

In the realm of high-performance computing (HPC), the A100 Tensor Core is equipped with new IEEE-compliant FP64 processing. It offers a substantial performance boost, delivering 2.5 times the FP64 processing capability of the V100. This advancement empowers researchers, scientists, and engineers in the HPC domain to tackle complex calculations and simulations with increased efficiency and speed.

The NVIDIA A100 GPU is designed to excel in accelerating both large complex workloads and smaller workloads with efficiency. It empowers data centers to handle unpredictable workload demands by offering fine-grained workload provisioning, increased GPU utilization, and improved total cost of ownership (TCO). With impressive speedups compared to the V100, the A100 delivers exceptional performance for AI training and inference workloads, as depicted in Figure 2. Furthermore, Figure 3 highlights significant performance enhancements across various HPC applications. The A100 sets a new standard for computational power and versatility in the realm of GPU computing.

The NVIDIA A100 GPU is a technical layout step forward this is fueled through 5 key innovations, and the final results of theseimprovements bring about 6x better overall performance than NVIDIA’s preceding technology Volta structure for education and 7x better overall performance for inference.The improvements are referred to below:

1. NVIDIA Ampere Architecture: At the center of this chip is the NVIDIA Ampere GPU structure that containsextra than fifty four billion transistors and hence, making it the most important 7-nanometer processor withinside the world
2. Third-generation Tensor Cores with TF32: The A100 consists of new TF32 for synthetic intelligence that lets in foras much as 20x the AI overall performance of FP32 precision, with out making any modifications withinside the code.
3. Multi-Instance GPU: Multi-example GPU or MIG is a brand new era that lets in a couple of networksTo perform concurrently on a unmarried A100 GPU for most useful usage of computing resources. It allows a unmarried A100 GPU to be partitioned into as many as seven separate GPUs so as to delivervarious levels of computing for jobs of various sizes whilst imparting premiere usage in addition to maximizing go back on investment.
4. Third-Generation NVIDIA NVLink: The 3rd-technology NVIDIA NVLink allows in doubling the high-tempo connectivity amongst GPUs, and for that reason provide inexperienced standard overall performance scaling in a server.
5. Structural Sparsity: Structural sparsity is a brand new approach that harnesses the inherently sparse nature of AI math to double the performance.

The advantages of GPU servers

• Enhanced Performance: GPU servers excel at parallel processing, making them highly efficient for computationally intensive tasks like AI, deep learning, scientific simulations, and data analysis. The parallel architecture of GPUs allows for faster execution of tasks compared to traditional CPUs.
• Accelerated Machine Learning: GPUs are widely used in machine learning applications. GPU servers provide the necessary computing power to train complex models and process large datasets, reducing training time and enabling faster iteration and experimentation.
• Scalability: Our GPU Server Hosting offer scalability options, allowing users to scale up or down their computing resources based on the changing needs of their applications. This ensures optimal performance and resource utilization, as users can allocate GPU resources as required.
• Customizability: GPU servers allow users to customize hardware configurations and software setups according to their specific requirements. This flexibility enables the creation of tailored server environments optimized for specific workloads.
• Cost-Effectiveness: GPU servers can provide cost savings compared to building and maintaining in-house GPU infrastructure. By utilizing GPU servers on a pay-as-you-go or subscription basis, users can access powerful computing capabilities without the upfront costs of purchasing and managing dedicated hardware.
• Remote Access and Collaboration: GPU servers can be accessed remotely, enabling users to utilize the computing power from anywhere with an internet connection. This allows for collaboration among distributed teams and facilitates remote work scenarios.
• Efficient Resource Utilization: GPU servers optimize resource utilization by distributing the workload across multiple GPUs. This parallel processing capability allows for higher throughput and more efficient utilization of computing resources.
• Infrastructure Stability: GPU servers are typically built on robust and reliable infrastructure, ensuring stable and uninterrupted operation even under heavy workloads. This reliability is crucial for mission-critical applications and prevents downtime and disruptions.
• Virtualization Support: Many GPU servers support virtualization technologies, allowing for the efficient sharing and allocation of GPU resources among multiple users or virtual machines. This enables better resource utilization and cost savings in multi-tenant or virtualized environments.
• Real-time Insights and Management: GPU servers often come with monitoring and management tools that provide real-time insights into GPU utilization, performance metrics, and resource allocation. These tools facilitate efficient management, troubleshooting, and optimization of the GPU server infrastructure.