Deep Learning is a high computationally demanding field and your choice of GPU will determine your deep learning experience. The longest and most resource-intensive stage of most deep learning implementations is the training phase. Especially as the number of your parameters increases, the longer your training time lasts. That means your resources are being used up longer and you will have to wait and waste your precious time.
Graphics processing units (GPUs) can help reduce these costs as they can help you run your training tasks in parallel, distribute tasks across processor clusters, and execute simultaneous calculation operations. Therefore, you can quickly and efficiently run models with large numbers of parameters.
Choosing a GPU is extremely important because it can save your cost significantly, increase your work efficiency, and save your valuable time.
When choosing a GPU, we need to consider the following factors:
When choosing a GPU, you should consider the ability to link the units first. Connect GPUs directly link your deployment scalability and multi-GPU utilization and distribution strategies.
Normally, consumer GPUs do not support connectivity. And, currently, Nvidia has NVLink connectivity for the RTX 2080 Ti and RTX 3090 GPUs. And these are also the two most used cards for training nowadays, especially for complex tasks.
The Nvidia GPU has the best support for the machine learning library and integrates with popular frameworks, such as PyTorch or TensorFlow. The NVIDIA CUDA Toolkit includes GPU acceleration libraries, C and C ++ compiler and runtime as well as optimization and debugging tools. It allows you to get started right away without having to worry about building custom integrations.
- The Tensor core helps reduce the cycles used needed to calculate multiplication and addition operations.
- The Tensor core helps to reduce dependency on repeated shared memory accesses, thus saving additional cycles for memory access.
- Using a Tensor core saves you from having to worry about bottlenecks when calculating. The only bottleneck is getting data to the Tensor cores.
Tensor Cores are fast. And in fact, they’re so fast that they don’t seem to work because they’re waiting for the memory to come from the mass memory. For example, in training using large matrices – bigger is better for Tensor Cores – we use Tensor Cores TFLOPS at about 30%, which means 70% of the time, Tensor Cores is inactive.
That means when comparing two GPUs with Tensor Cores, one of the best indicators for GPU performance is their memory bandwidth. For example, the GPU RTX 3090 has a memory bandwidth of 935.8 GB/s compared to 760GB/s of the RTX 3080, and compared to the RTX 2080 Ti is 616GB/s. So the basic estimate of the RTX 3090’s acceleration compared to the RTX 3080 is 1.23 times and that of the RTX 2080 Ti is 1.52 times.
First, if you choose a GPU, you need to make sure that it has enough memory for what you want to do. And other issues such as: Are there any additional warnings for the GPU I chose? For example, if it’s an RTX 3090, can I install it in my computer? Does my power supply unit (PSU) have enough capacity to support my GPU (s)? Will heat dissipation a problem or can I somehow cool the GPU effectively?
You should have at least 11 GB of memory for general training tasks, and 24GB or more if you are working on more complex algorithms. Having less than 11GB can create difficult situations when running certain models.
The RTX 30 series is very powerful and I recommend these GPUs. You might expect 30% or more faster training, but it could be a big deal solving all the other RTX 30 GPU issues: Memory, power supply, coolers, requirements in terms of power, or even the fact that you need to sell your old GPU …
At iRender, we provide a fast, powerful and efficient solution for Deep Learning users with configuration packages from 1 to 6 GPUs RTX 3090 on both Windows and Ubuntu operating systems. In addition, we also have 6 GPUs configuration packages of RTX 3080 and RTX 2080 Ti on windows OS (and will certainly be on Ubuntu very soon). You won’t have to worry about how much the RTX currently costs, how easy it is to buy it, or the problems with installing and maintaining it like we mentioned above. Moreover, with the 24/7 professional support service, the powerful, free and convenient data storage and transferring tool – GPUhub Sync, along with an affordable cost, make your training process more efficient.
See how to training Tensorflow on Ubuntu with RTX 3090 of iRender:
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Reference Source: timdettmers.com; www.run.ai
Thank you & Happy Training!