Deep learning requires special hardware to operate, and one of the major breakthroughs was the NVIDIA V100. It came out in 2017 and became the first GPU that have Tensor Cores. Thus, the V100 increased the speed of deep learning training. It remains a highly capable GPU for AI research and data science. It is still used by many AI researchers and data scientists.
What is the NVIDIA V100 GPU?
The NVIDIA V100 is a data center GPU graphics card. It is based on the NVIDIA Volta architecture.
Further, it primarily aims at accelerating AI, data science, and HPC. It has a lot of powerful components. The V100 has thousands of CUDA cores, special Tensor Cores, and comes with high-speed memory & high memory bandwidth. This GPU is not a gaming card. It is a workhorse for servers and supercomputers. It helps to solve complex problems. When it was launched, it was a big difference in the AI world.
The Innovations of the V100 GPU
The V100 brought many new features. All these features made it a huge step forward in AI. They changed the manner in which people did deep learning.
Tensor Core Technology
The biggest innovation in the V100 was the Tensor Cores. These are specialized cores in the GPU designed to do matrix maths. This is the primary form of deep learning calculation.
The V100 had 640 Tensor Cores. They gave it a massive performance jump for AI. It delivers up to 12x faster Tensor Core-accelerated FP16 training compared to its predecessor. For standard FP32 workloads, the speedup was smaller but still significant. This enabled models to be trained much faster and sped up AI research a lot.
High-Bandwidth Memory 2 (HBM2)
The V100 came with HBM2 memory that is much faster than traditional memory. The V100 had 16GB and 32GB versions. It gave a memory bandwidth of 900 GB/s (32GB SXM2 version). Moreover, the PCIe version has a bandwidth of 870 GB/s.
This large bandwidth is highly essential for large datasets. It helps to move data between the memory and the cores. This prevents a bottleneck and enables the GPU to process information immediately.
NVLink Interconnect
The V100 also featured a new NVLink, i.e. a special connection between GPUs. It lets several V100 GPUs communicate with one another at a very high speed.
It could deliver an aggregate bidirectional bandwidth of up to 300 GB/s per GPU when multiple NVLink connections were active. It is significantly faster than the regular PCIe connection. You can have a lot of GPUs working together with NVLink. Also, one can train a very large model using a large number of GPUs simultaneously. This aspect assists in major research studies.
Comparison of NVIDIA V100 and NVIDIA P100
The V100 GPU replaced the P100 GPU. It introduced many significant changes. Some of the main differences between them are presented in this table.
You will see, the V100 has been a huge enhancement of the P100. It had more CUDA cores and was far faster with the addition of Tensor Cores to AI. It also had better memory bandwidth. NVLink of the V100 was also much faster. This rendered it a better option for serious deep learning.
| Feature | NVIDIA P100 | NVIDIA V100 |
| Architecture | Pascal | Volta |
| Process Node | 16nm | 12nm |
| CUDA Cores | 3,584 | 5,120 |
| Tensor Cores | No | Yes (640) |
| Memory Capacity | 16GB HBM2 | 16GB or 32GB HBM2 |
| Memory Bandwidth | 720 GB/s | 900 GB/s |
| AI Performance (FP16) | 21.2 TFLOPS | 125 TFLOPS |
| FP32 Performance | 10.6 TFLOPS | 15.7 TFLOPS |
| NVLink Bandwidth | 160 GB/s | 300 GB/s |
Cantech’s V100 GPU Servers
Cantech offers high-performance GPU solutions to AI and data science. We know that not all projects require the latest GPU. The V100 remains an excellent choice in most jobs. Our services are available in V100 based on your project needs. You get assured high performance through our GPU solutions.
They are scalable, so you may begin with one GPU and obtain a cluster with many GPUs. We have a flexible infrastructure too. You can select the appropriate power for your project.
We have an exceptionally well-versed technical team. They can help you with your V100-based projects setup, optimization, troubleshooting, etc. We provide 24/7 support to make sure your work goes smoothly.
Conclusion
NVIDIA V100 has received a niche in the AI world. It was the first GPU that had Tensor Cores, and deep learning became accessible to more people. The V100 GPU remains a strong option for AI and data science workloads, though newer GPUs like the A100 and H100 now deliver higher performance and larger memory capacity. It works well with complicated deep learning models. Simulations and processing of large data sets are easy to do. This power is easily reached through our V100 servers.
FAQs
What is the difference between CUDA cores and Tensor Cores?
CUDA cores are general-purpose cores. They are capable of making numerous calculations and suit parallel duties. Whereas Tensor Cores are specialized cores. They are designed to do matrix math. Also, deep learning calculations are much faster using them. The V100 became the first to have both.
Is the V100 still a good GPU for deep learning today?
Yes, the V100 can still be used in deep learning. It is a very capable GPU that can train numerous complex models. It is not as fast as newer GPUs with very large models, but it remains an excellent selection in most of the research projects and learning. Its performance is sufficient to a great extent.
What is the main benefit of using a V100 for data science?
Its processing power is its greatest advantage. Data science has big data and complicated computations. V100 can handle data at a high speed with its high memory and bandwidth. This accelerates the data analysis and model training. It assists data scientists in achieving faster results.
Can V100 GPUs be used together in a single server?
Yes, the V100 has NVLink technology. This enables multiple V100 GPUs to be connected. They have the ability to work together as one unit. It works well in the training of very large neural networks. It is also useful in running complex simulations. It is one of the major characteristics of scalable systems.
Why did the V100 change deep learning?
Tensor Cores were introduced in the V100. This was a big change. It rendered deep learning more effective. Training times went down, and researchers were able to test bigger models. The V100 introduced up to 32GB of HBM2 memory, which at the time allowed researchers to train larger models and handle bigger datasets. While newer GPUs now surpass this capacity, the V100’s memory was a major leap forward in 2017.
