One of the most daunting tasks of implementing a deep learning model is training it. During this phase, developers have to wait for the model to get acquainted, resulting in a waste of time and resources.
GPUs are the soul of AI that can help tackle the training-phase challenges. No, they will not eliminate the training phase as it is essential for deep learning but will allow the developers to train the model and perform AI operations parallelly. GPUs will also enable you to run and deploy large training sets efficiently. They are the core drivers paving the way for the future of practical AI. Thus, it is evident that choosing the right GPU is essential for deep learning.
Top GPUs for Deep Learning Training
We are writing this post from an enterprise perspective. Hence, we will begin with the best GPUs for large-scale projects and data centres. It is best to have these large-scale project GPUs on the cloud for leveraging cloud-native benefits and features. For instance, you can use GPUs deployed on E2E Cloud to run all your large and small-scale deep learning projects.
1. NVIDIA Tesla V100
The NVIDIA Tesla V100 is one of the most-advanced, Tensor core-based data centre GPUs. It is specifically designed to accelerate AI and deep learning performance. A single V100 server is adept at providing the performance of hundreds of traditional CPUs. Thus, with the NVIDIA Tesla V100 GPU, you get the optimal performance to develop the next deep learning breakthrough. Some key features of this high-performing GPU are:
- Based on Volta Architecture
- 640 Tensor cores
- 130 teraflops (TFLOPS) performance
- Next-generation NVLink
- Maximum efficiency mode for higher compute at lower power consumption
- 16 GB VRAM
- 900 GB/s raw bandwidth
- 16 GB and 32 GB configurations
- PCI-E interface
2. NVIDIA A100
The NVIDIA A100 is the most advanced AI and deep learning accelerator for enterprises. It has got the resources to cater to all your needs, no matter how vast they are. With capabilities like high-performance computing (HPC), enhanced acceleration, and data analytics, the GPU can help you overcome all computing challenges. It has the power and efficiency to scale up to thousands of GPUs and divide the workload over multiple instances. Some of the core A100 features include:
- Based on multi-node Ampere architecture
- Up to 624 teraflops performance for deep learning
- 20x more TOPS compared to Volta GPUs
- Next-generation NVLink for enhanced interconnection
- Multi-instance GPU (MIG)
- 1.6 TB/s raw memory bandwidth
- 40 GB high performing GPU memory
- Up to 600 GB/s interconnect bandwidth
Selecting the right GPU for your deep learning project depends on your specific needs. Determine what you want to do with deep learning and how much bandwidth you will need. However, the most important thing to remember is that consumer-based GPUs can only handle a limited number of parameters. Hence, if you want to scale efficiently and provide a massive amount of parameters, it is best to go for data center GPUs on the E2E cloud. With the E2E cloud, you can efficiently run and deploy your deep learning models with cost-efficiency. The pay-as-you-go pricing model ensures that you pay only for what you use and get the best within your budget.
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