When buying a GPU for machine learning, there are several factors to consider. Here are some key aspects to look into:
GPU Architecture: The architecture of the GPU is crucial as it determines its computational capabilities and performance for machine learning tasks. Look for modern architectures, such as NVIDIA's Turing or Ampere, which offer dedicated hardware for machine learning workloads.
CUDA Cores: CUDA cores are parallel processors within the GPU that perform the heavy lifting for machine learning computations. More CUDA cores generally lead to faster training and inference times. Consider GPUs with a higher number of CUDA cores for improved performance.
Memory (VRAM): The amount of video RAM (VRAM) on the GPU is critical for deep learning models, especially those with larger datasets or complex architectures. Choose a GPU with sufficient VRAM to accommodate your training data and model requirements. Aim for at least 8GB or more of VRAM for most machine learning tasks.
Memory Bandwidth: The memory bandwidth of the GPU affects how quickly data can be read from and written to the VRAM. Higher memory bandwidth allows for faster data transfers, which can improve overall training performance.
Tensor Cores (for AI-specific workloads): Tensor cores are specialized hardware components found in some GPUs, such as NVIDIA's RTX series. They accelerate matrix operations commonly used in deep learning, offering significant performance gains. If you'll be working with AI-specific workloads, consider GPUs with tensor cores.
Compatibility and Software Support: Ensure that the GPU you choose is compatible with the deep learning frameworks and libraries you plan to use, such as TensorFlow or PyTorch. Also, check for reliable driver support and compatibility with your operating system.
Power and Cooling: Consider the power requirements of the GPU and ensure that your system's power supply can handle it. Additionally, check if your system has adequate cooling to handle the GPU's thermal requirements, as machine learning workloads can generate substantial heat.
Budget: Finally, consider your budget and strike a balance between performance and cost. Higher-end GPUs tend to offer better performance but come at a higher price. Evaluate your specific needs and choose a GPU that meets your requirements without exceeding your budget.
It's worth noting that GPU selection depends on the specific machine learning tasks you'll be performing. For more complex models or larger datasets, a higher-end GPU with more resources is generally recommended. However, for simpler models or smaller datasets, a mid-range GPU may suffice.
A CPU (Central Processing Unit) is the workhorse of your computer, and importantly is very flexible. It can deal with instructions from a wide range of programs and hardware, and it can process them very quickly. To excel in this multitasking environment a CPU has a small number of flexible and fast processing units (also called cores).
A GPU (Graphics Processing Unit) is a little bit more specialised, and not as flexible when it comes to multitasking. It is designed to perform lots of complex mathematical calculations in parallel, which increases throughput. This is achieved by having a higher number of simpler cores, sometimes thousands, so that many calculations can be processed all at once.
This requirement of multiple calculations being carried out in parallel is a perfect fit for:
graphics rendering — moving graphical objects need their trajectories calculated constantly, and this requires a large amount of constant repeat parallel mathematical calculations.
machine and deep learning — large amounts of matrix/tensor calculations, which with a GPU can be processed in parallel.
any type of mathematical calculation that can be split to run in parallel.
Tensor Processing Unit (TPU)
With the boom in AI and machine/deep learning there are now even more specialised processing cores called Tensor cores. These are faster and more efficient when performing tensor/matrix calculations. Exactly what you need for the type of mathematics involved in machine/deep learning.
Although there are dedicated TPUs, some of the latest GPUs also include a number of Tensor cores, as you will see later in this article.
Nvidia vs AMD
Nvidia’s GPUs have much higher compatibility, and are just generally better integrated into tools like TensorFlow and PyTorch.
trying to use an AMD GPU with TensorFlow requires using additional tools (ROCm), which tend to be a bit fiddly, and sometimes leave you with a not quite up to date version of TensorFlow/PyTorch, just so you can get the card working.
CUDA Cores and Tensor Cores
This is fairly simple really. The more CUDA (Compute Unified Device Architecture) cores / Tensor cores the better.
RAM and chip architecture should probably be considered first, and then look at cards with the highest number of CUDA/tensor cores from your narrowed down selection
For machine/deep learning Tensor cores are better (faster and more efficient) than CUDA cores. This is due to them being designed precisely for the calculations that are required in the machine/deep learning domain.
The reality is it doesn’t matter a great deal, CUDA cores are plenty fast enough. If you can get a card which includes tensor cores too, that is a good plus point to have, just don’t get too hung up on it.
CUDA cores — these are the physical processors on the graphics cards, typically in their thousands.
CUDA 11 — The number may change, but this is referring to the software/drivers that are installed to allow the graphics card to work. New releases are made regularly, and it can be installed like any other software.
CUDA generation (or compute capability) — this describes the capability of the graphics card in terms of it’s generational features. This is fixed in hardware, and so can only be changed by upgrading to a new card. It is distinguished by numbers and a code name. Examples: 3.x [Kepler], 5.x [Maxwell], 6.x [Pascal], 7.x [Turing] and 8.x [Ampere].
references:
https://towardsdatascience.com/how-to-pick-the-best-graphics-card-for-machine-learning-32ce9679e23b
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