Best GPU for Deep Learning
Optimize for deep learning with these GPU picks
The best GPUs for deep learning are those that can process the most parallel calculations and data simultaneously. These new deep learning GPUs are made to handle modern software libraries like TensorFlow and PyTorch out of the box with little to no configuration needed, as well as to provide high-performance computing (HPC) capabilities on a single chip.
The ideal GPU should take into account a variety of parameters, including price, power consumption, memory size, tensor cores, and more because deep learning programs require a significant amount of processing power to function well. The strongest GPUs available today for deep learning will be discussed in detail in this post.
Our top picks
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01Best GPU for Deep Learning
ASUS ROG Strix RTX 4090 OC
- 022nd Best GPU for Deep Learning
ASUS TUF Gaming Nvidia GeForce RTX 3080 OC
- 033rd Best GPU for Deep Learning
Best GPU for deep learning
ASUS ROG Strix RTX 4090 OC
Core Clock Speed
2,640 MHz boost
CUDA Cores
16,384
Memory Size
24GB GDDR6X
Dimensions
357.6 x 149.3 x 70.1mm
PSU Required
1000W
TDP
450W
As the top selection of the Ada series, the top of the range comes with some serious power. The RTX 4090 comes with plenty of power, and even though it might not be the card you expect for professional use, with such a spec it comes as no surprise it has the power to do it. So it offers a great performance for what it has to offer.
And the Strix is a great choice for it. The card comes with 16,384 CUDA cores, along with 24GB of GDDR6X memory. And clocked at 2,640 MHz it can really give out great performance, but at the cost of 450W. And the card itself is huge, and so does potentially cause some issues. You can read more about the card, in our Strix RTX 4090 review.
Second best GPU for deep learning
ASUS TUF Gaming Nvidia GeForce RTX 3080 OC
Clock Speed
1815MHz Boost Clock (OC)
VRAM
10GB GDDRX
Thermal Design
Axial-tech Triple Fan
The GeForce RTX 3080 is a top-notch GPU for creating deep-learning software. Large datasets may be handled by its enormous memory, which also offers quick performance for running algorithms and analyzing big data. With regard to specifics, the RTX 3080 has 10GB of GDDR6X memory and a high clock speed of 1,800 MHz, which is similar to the previous generation but faster than the typical CPU clock speed. The TU102 core with 8,960 CUDA Cores on this GPU is another factor making it an excellent choice for deep learning.
And the TUF card is a good contender for a food model card. From previous reviews of the type, we know it is well-built and performs well. With a well-built cooling solution, it means the card will perform over time with no struggles as the core is cooled well and can keep the performance up with no throttling.
Best enterprise GPU for deep learning
One of the newest and best GPUs available is the NVIDIA RTX A6000, which is a fantastic option for deep learning. The A6000 GPU’s Turing architecture enables it to execute both deep learning algorithms and conventional graphics processing operations. The large datasets you’ll need for training your neural networks can be handled by it because of its enormous 48 GB memory interface.
Additionally, you can train your models faster than with previous generations thanks to its eight trillion floating-point operations per second (TFLOPS) performance. Deep Learning Super Sampling is another function of the RTX A6000. Using this technology, you may generate images at larger resolutions while keeping the same level of speed and quality.
How we choose and test
When it comes to picking out the best GPU for deep learning there are plenty of options. And so without experience and knowledge, we know what to look out for and know what to look for in any graphics card and what you might expect to have as the best choice for your work.
When hands-on we can put the card through the ringer, testing out how they perform and what they are capable of achieving. This gives us a good understanding of what we recommend. But also knowledge of the cards and how they have previously been giving us a good understanding. Along with looking at reviews of others, we get a good picture of the best options.
Other GPU guides
FAQs
Which GPU is best for deep learning?
For deep learning, it’s best to choose cards with plenty of VRAM and processing cores. These will work faster, and as such the RTX 4090 is a good gaming GPU to use, but the A6000 is a good enterprise GPU to go for that is more made for those tasks.
Do I need a good GPU for deep learning?
You don’t really need the best choice of GPU for deep learning, but it definitely quickens and improves the experience. BUt any GPU better than your CPU will improve your deep learning process.
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