Choosing the Best GPU for Deep Learning & AI (2020)

CPUs are too versatile for the AI utilisation. Therefore, GPUs are often used. Nevertheless, some manufacturers like Nvidia and AMD are already working on more specialized hardware for machine learning and AI. GPUs that are specifically optimized for AI can perform hundreds of parallel calculations, resulting in over 100 tera floating-point operations per second (TeraFLOPS).

• Artificial intelligence requires only a fraction of the instruction set of conventional CPUs.
• GPUs are particularly popular because of their easy availability and programmability, but more and more manufacturers are developing special AI hardware.

Some companies offer specially optimized AI chips like NVIDIA GPUs to optimize their systems for AI and analytical applications. (RTX & Titan Series)

State-of-the-art (SOTA) deep learning models need a lot of memory. Regular GPUs don’t have enough VRAM to meet the requirements for procession SOTA deep learning models. GPUs with a larger GPU memory are performing better because they can take larger batch sizes which in turn helps to fully utilize the CUDA cores.

So, in other words, the CPU is good at fetching small amounts of memory quickly (5 * 3 * 7) while the GPU is good at fetching large amounts of memory (Matrix multiplication: (A*B)*C). The best CPUs have about 50GB/s while the best GPUs have 750GB/s memory bandwidth. So the more memory your computational operations require, the more significant the advantage of GPUs over CPUs.

You can find most of the RTX series of Nvidia GPU's in Gaming Laptops and GTX series in most of the mid-higher ranger computational laptops. the best configuration to use GPU along with CPU's is i7 & i9 Intel CPUs.

There are a few things that need to focus on while getting a GPU:

1. CPU - Intel i7 or i9 Processors (9th Gen are good to go at least)
2. GPU - Look at Tensor Cores, Cuda Cores
3. RAM - 32 GB at least DDR4

(Note: you won't find Cuda in AMD, as it goes with Stream Processors)

Here are some of the Benchmarks you can look at,

Below are some of the benchmarks of the GPU,

GPU Recommendations

• RTX 2060 (6 GB): if you want to explore deep learning in your spare time.
• RTX 2070 or 2080 (8 GB): if you are serious about deep learning, but your GPU budget is $600-800. Eight GB of VRAM can fit the majority of models.
• RTX 2080 Ti (11 GB): if you are serious about deep learning and your GPU budget is ~$1,200. The RTX 2080 Ti is ~40% faster than the RTX 2080.
• Titan RTX and Quadro RTX 6000 (24 GB): if you are working on SOTA models extensively, but don't have the budget for the future-proofing available with the RTX 8000.
• Quadro RTX 8000 (48 GB): you are investing in the future and might even be lucky enough to research SOTA deep learning in 2020.

This is the Top-level Info, if you need more information in detail then visit Nvidia Developers and Lamda blog.

you can check your CUDA core count and the GPU Frequency from here: https://developer.nvidia.com/cuda-gpus

Reference: Lamda + Tim Dettermer + Nvidia

#nvidia #lamda #RTX #GTX #Titan #AI