The world today requires high-powered computers. Consequently, artificial intelligence models are becoming bigger, so they require special hardware for training. NVIDIA A100 is one such GPU. It is quite a step up from the previous generation of GPUs. We can say that this is a workhorse in modern data centers. It deals with large-scale AI training and high-performance computing. A100 is one of the essential elements for companies carrying out cutting-edge research.
Let’s discuss more about how the A100 GPU is an ideal option for AI Training & Server-Scale Deep Learning.
What is the NVIDIA A100 GPU?
NVIDIA A100 is a data center GPU based on the NVIDIA Ampere architecture. The primary task of this GPU is to process AI and HPC tasks. There are a few very special features and optimizations designed specifically for accelerating demanding computations. The A100 contains a huge number of CUDA cores and Tensor Cores. These cores are specifically designed for AI calculations.
Further, it also has high-bandwidth HBM2e memory. NVIDIA A100 is available in two primary versions, which are 40GB and 80GB of memory.
The A100 does not target gaming computers. It is designed specifically for servers and helps in training large language models. A100 is a universal accelerator that supports any type of data center workload.
A100’s Key Innovations
The A100 GPU brings many new technologies. The technologies provide a huge performance advantage and make it highly effective for different tasks.
Third-Generation Tensor Cores
The A100 has new Tensor Cores, faster than before. There are numerous types of math that they can do. Also, they help in training and inference of AI.
The new Tensor Cores have a new math format. This is referred to as Tensor Float 32 (TF32). It provides a massive performance advantage that is 20 times faster for AI training than the previous generation, without needing any code changes. Tensor Cores also support FP64 for scientific computing. This makes the A100 great for HPC tasks too.
Multi-Instance GPU (MIG)
The A100 has a very special feature, MIG. It allows you to partition a single GPU into as many as seven independent GPUs. These smaller sections are totally separate from each other. Each of them possesses its own memory and cores. This is an excellent feature for companies, as they can allocate GPUs for different teams or tasks and further boost the GPU utilization. Thus, it can be more cost-effective than dedicating an entire GPU to smaller tasks.
Structural Sparsity
AI models often contain large numbers of zero values. These values do not have a significant impact on the results. A100 GPU can take advantage of this and can skip the zero values. This assists it in making calculations faster and doubling the performance of certain tasks. It accelerates AI inference significantly, which is important to real-time applications.
NVIDIA A100 vs. NVIDIA V100: A Comparison
A100 GPU is the successor of the V100 GPU. It brings many improvements. Some of the notable differences between them are indicated in this table.
| Feature | NVIDIA V100 | NVIDIA A100 |
| Architecture | Volta | Ampere |
| Process Node | 12nm | 7nm |
| CUDA Cores | 5,120 | 6,912 (for PCIe version) |
| Tensor Cores | 2nd Gen | 3rd Gen |
| Memory Capacity | 16GB or 32GB HBM2 | 40GB or 80GB HBM2e |
| Memory Bandwidth | 900 GB/s | Up to 2.0 TB/s |
| FP32 Performance | 15.7 TFLOPS | 19.5 TFLOPS |
| AI Performance (FP16) | 125 TFLOPS | 312 TFLOPS (624 TFLOPS with sparsity) |
| FP64 Performance | 7.8 TFLOPS | 9.7 TFLOPS (19.5 TFLOPS with Tensor Cores) *additional acceleration available via Tensor Cores in certain operations |
| Multi-Instance GPU (MIG) | No | Yes (up to 7 instances) |
The A100 graphics card is far stronger with better architecture. It has more memory and bandwidth. The new Tensor Cores in the A100 give a huge performance boost. Its MIG capability is a game-changer for data centers.
Cantech’s NVIDIA A100 GPU Servers
We at Cantech understand your AI needs. We offer powerful solutions with NVIDIA A100 GPUs. Your AI training projects are ideal for our services. We provide on-demand access to A100 servers. This gives you the power and enterprise-grade performance you need without a big investment.
Our service can be used with the MIG feature of the A100. This assists you in maximizing the value of the GPU and running several small tasks on one A100 card. This is great for development and testing. We ensure that you get the right resources for each job.
Moreover, we offer 24/7 special technical support and a 99.97% uptime guarantee. Also, you get full root access. Check out our affordable NVIDIA A100 GPU solutions.
Conclusion
The NVIDIA A100 is a true AI leader. It is designed for serious AI training and deep learning. It is unbeatable with its advanced Ampere architecture, strong Tensor Cores, and MIG feature. Thus, the A100 offers unbelievable performance and efficiency. It assists researchers and companies in training large models quickly and reliably. It is the most appropriate selection of all those doing server-scale deep learning. The A100 is a smart future AI investment.
FAQs
What is the main difference between NVIDIA A100 and a gaming GPU?
NVIDIA A100 is a data center GPU. It is built for AI and HPC. It possesses such special features as Tensor Cores and MIG. On the other hand, Gaming GPUs are made for graphics. They are good for gaming. They do not have the same features and do not have as much memory as the A100.
What are the best workloads for the A100 GPU?
Large-scale AI training is best done with an A100 GPU. It is great for deep learning. It is also ideal for high-performance computing. Further, it assists in data analytics and scientific simulations too. A100 is meant to support complex and data-intensive workloads.
What is Multi-Instance GPU (MIG) technology?
MIG is a feature on the A100 GPU. It lets you partition one GPU and create up to seven smaller GPUs out of it. These small instances of GPUs are each hardware-isolated and have their own resources. This is useful in running a number of jobs simultaneously, improving the GPU utilisation.
How does the A100 improve AI training performance?
The A100 enhances performance in numerous aspects. It has new Tensor Cores. They accelerate mathematical operations. It gets a big boost with its new TF32 format. Further, the A100 also has a huge memory and great memory bandwidth. This assists in handling large datasets quickly.
Is A100 also good at AI inference?
Yes, the A100 is good at AI inference. Its structural sparsity is quite useful as it can ignore unnecessary information. This makes inference up to 2x faster for some models. This comes in handy with cloud services.
