AMD GPUs and things that no one will tell you about it
Published: 2022-12-15 15:00:22


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In my first year of college I got my laptop and in a rush, I bought an AMD GPU with it. Within a few months, I realized that the frame rates drop during gaming and it heats pretty easily. Ok, that’s not a big issue for me as I don’t play a lot of games. I mostly use my laptop for programming purposes. I recently started learning AI programming and headed to build my first AI project unaware of the situation that AMD is going to ditch me.

We all know Artificial intelligence is the upcoming reality which has raised the demand for processing more and more data to produce meaningful outcomes. AI requires lots of computation power for training the model. This is where CPUs sucks and GPUs make its entry. GPUs have a different architecture from CPUs, it works in a parallel way whereas CPU goes sequentially. GPU considerably reduces the time required for model training.

If you are the kind of person that loves AI and machine learning and building your own AI model is your next goal then the first and foremost thing is that you must have a laptop backed by a good GPU for training your machine learning model(which I was unaware). When it comes to GPU there two big names NVidia and AMD. Both are always been rivals in the field of graphic processors but we all know that NVidia is the undisputed king of this field.

AMD also has a long history in manufacturing good CPUs but when it comes to GPU AMD doesn’t stand anywhere close to NVidia. But still, AMD managed to produce good GPUs that kind of does the job for you.

If you got NVidia on your laptop then everything will go smoothly for you. In case you purchased an AMD graphic card then you are going to experience a hard time.

NVidia has CUDA support for accelerating graphic processing and deep neural network model training tasks. Installing CUDA for machine learning tasks is like a cakewalk. AMD has also provided a ROCm platform environment for all types of GPUs but the environment is new and does not support all hardware.

##CUDA

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers can dramatically speed up computing applications by harnessing the power of GPUs.


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The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools, and the CUDA runtime.

##ROCm

ROCm is a universal platform for GPU-accelerated computing. A modular design lets any hardware vendor build drivers that support the ROCm stack . ROCm also integrates multiple programming languages and makes it easy to add support for other languages.


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ROCm even provides tools for porting vendor-specific CUDA code into a vendor-neutral ROCm format, which makes the massive body of source code written for CUDA available to AMD hardware and other hardware environments.

CUDA Toolkit is available for both Windows and Linux platforms whereas ROCm ditched Windows user, providing support for only Linux users.


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ROCm has little or no community supports as it has been recently developed and will take time to build its community. The Internet is flooded with CUDA support which makes life easier for NVidia users.

When I started building my first image recognition model I needed to train my model on a huge data set. Training the model using CPU took hours to complete. I switched to GPU but as I’m a windows user I can’t use ROCm for my AMD graphic card. It almost like a dead piece to me.

AMD has not stated anything regarding windows support and we are also not very optimistic about it. It will take a few years to have good community support for ROCm. Until then we are only left with a single option which is to buy NVidia GPUs.