With the recent explosion of interest in deep learning, there have been many debates about how much VRAM you need to run it. Some claim that lower end graphics cards are enough, while others insist that higher end ones are needed to achieve good performance.
In this article we will talk about what types of GPUs require more or less GPU memory and whether or not they’re essential for running neural networks. We will also look at some strategies for optimizing your model by reducing overhead such as cache misses and using parallel execution modes.
We will be comparing two different state-of-the-art architectures for object recognition: ResNet50 and InceptionV3. Both use very similar numbers of parameters but one uses twice as much VRAM per parameter as the other!
So which is better? That depends on your computer’s hardware and your budget. If you have a high RAM limit and only have access to low end CPUs then going with the second option may be your best bet since it requires fewer GB of VRAM per layer than the first one does.
However, if you have lots of money to spend or want maximum speed then the first one is probably the way to go. You get more bang for your buck because you can add more layers without having to worry about out of memory errors due to lack of RAM.
This article will assume you know how to navigate through the browser so no knowledge of those tools is required.
Reasons to get more VRAM
Having more VRAM means you can hold more programs at a time in your GPU. This is important as most deep learning software requires lots of running, so having more VRAM allows you to run longer and faster before needing to save and restore or reload files.
Most laptops only have between 16-64GB of total memory, which isn’t very much unless you’re a computer scientist who has lots of apps installed! A normal person probably doesn’t need more than 32GB, however.
A lot of people are starting to use GPUs now for some reason, though, so it’s definitely not out of place to invest in one if you know what they do and how to work with them.
Luckily, buying a new laptop with a decent sized GPU will almost always include RAM as well, making the two compatible together! You could even just buy a cheap GPU and upgrade the RAM later.
Ways to get more VRAM
There are two main components of GPU memory-VRAM and bandwidth. The amount of VRAM in your computer directly correlates with the size of your GPUs internal cache, while the speed at which it can communicate with the GPU determines how fast it can process information.
The faster the bandwidth, the better! Having more VRAM is great, but making sure you have enough bandwidth is what really matters.
If you’re looking to maximize the performance of deep learning applications, then make sure to check out our article here about where to invest in DRAM to boost RAM. But as far as VRAM goes, we have some good news for you!
There are several ways to improve the graphics processing power of your machine by investing in additional VRAM.
Buy a new GPU
Recent developments in deep learning have shifted focus onto very large neural networks that can ingest vast amounts of data to learn complex patterns. These so-called “deep” networks are made possible by increasingly powerful graphics cards with faster processors.
The more processing power you give your computer, the better it will perform when training these networks. Since most of the costs associated with owning a GPU come from buying the hardware and software, buying a newer model is usually the best bet.
If you already have an older GPU, try looking into whether there are any discount programs or promotions at specific vendors. There may be monthly rewards systems where you earn points towards future purchases, too!
And remember, even if you don’t use all of the features available on your current card, leaving some chips off the GPU will help preserve performance.
Run GPU intensive programs in the background
While having more VRAM can help with deep learning, using as much RAM as possible is usually your best bet. Because GPUs are very expensive, most of the time people will be wasting money by buying an overpriced graphics card that they will not use to its full potential!
If you want to run some computer vision or natural language processing (NLP) software then it is advised to only upgrade your GPU’s memory budget rather than adding a new GPU. This is because the additional GPU resources will be wasted due to there being no other applications running on it!
Running computer vision or NLP software in the background is also a good way to save CPU cycles while still getting good results.
Use GPU-optimized software
When it comes to choosing your GPUs, one of the first things you should consider is whether or not they have enough VRAM.
VRAM is short for “video RAM” and this memory is used to store all of the applications that are being run by the GPU. More VRAM means more space to store apps which can potentially be larger files or even streaming apps like Netflix or YouTube!
The more VRAM that a GPU has, the faster it will operate due to having extra room to work with. Because most CPUs and graphics cards only use 3GB-6GB of VRAM at the very best, many people do not get the maximum performance out of their equipment.
Fortunately, there are ways to upgrade your GPU’s VRAM to better utilize all of its potential. You can either buy an external NVME SSD that uses M2 storage (which is much less expensive than A3) or just make sure that you are buying a GPU that has at least 8GB – 16GB of onboard VRAM.
Practice good GPU usage habits
While having more VRAM is great, doing so can be expensive if you are not careful with it!
Practice good GPU usage habits to ensure that your computer does not run out of memory due to an inexperienced user.
GPUs work by storing large amounts of data in fast memory which is then transferred into slower RAM or disk space to process the rest of the information.
When this happens, the system may stop working properly as the software has no extra room to store all the information. This could result in a poor experience since some tasks will take longer than expected to complete!
To prevent this from happening, make sure to only open as many applications as you have memory for! Close down apps when you can, but do not close too much at one time otherwise you may need to start over.
Also, avoid running very graphic-intensive programs like Photoshop or video editing software while other programs are closed because these require lots of graphics processing.
Instead, use less powerful applications during those times to preserve your machine’s resources.
Update your GPU drivers
As we mentioned earlier, GPUs have more internal memory than CPU processors. This means that you can use much higher resolution images during training and testing.
The general rule of thumb is to have at least twice as many GB of RAM as there are GB in your GPU’s card storage. So if you had 24GB of graphics card space, then you would want at least 48GB of total RAM!
This is not very practical though since most computers do not come with enough RAM to meet that requirement. Fortunately, NVIDIA and AMD both release new driver versions every year so it’s easy to update them!
By updating your GPU driver software, you get access to newer features such as faster GDDR5 ram or better parallel processing which allows you to train deeper networks. You also need to make sure that you are using the latest version of their AI platform like TensorFlow or PyTorch!
Google and Facebook use their own proprietary frameworks so it’s hard to compare results across different vendors but AIDA64 benchmarks show that 1080ti performance has increased by around 30% due to improved driver settings.
Get a new hard drive
When it comes down to it, the GPU is not very efficient without a decent amount of memory. The more RAM you have, the faster your computer can process graphics and machine learning algorithms.
A general rule of thumb is that every GB of DDR4 RAM will give you around 4GB of VRAM. This makes sense because each GPU has an assigned area in video games where this VRAM is stored.
The best way to ensure you have enough RAM for deep learning is by getting a new internal or external hard drive. An internal SSD would be better than a normal magnetic disk drive (HDD). They are much faster!
You could also just buy another couple hundred dollars in RAM if you feel that your current setup is insufficient.