Best GPU for Deep Learning in 2024 – our top picks
The best GPU for Deep Learning is essential hardware for your workstation, especially if you want to build a server for machine learning. Straight off the bat, you’ll need a graphics card that features a high amount of tensor cores and CUDA cores with a good VRAM pool. That basically means you’re going to want to go for an Nvidia GeForce RTX card to pair with a CPU of your choice. We recommend a minimum of Ampere architecture Nvidia GPUs for deep learning tasks.
Whether you’re looking to make a data center or want the capabilities to create a deep neural network platform or just want to try out artificial intelligence, our buying guide goes over both powerful GPUs and offerings based on their cost-effectiveness. It’s all about creating deep learning models, so you’ll need a video card with the specifications and memory bandwidth to handle AI workloads with high performance computing acceleration. Memory capacity also plays a factor in your PC.
There are so many different types of deep learning to be aware of from large-scale storage, data science, and data analytics for enthusiasts and dedicated data scientists alike. It’s all about precision after all, so whether you’re considering an AMD GPU or an Nvidia RTX model, we have you covered. For more, we’re bringing you our picks for the best graphics card as well as the best budget graphics card, and the best GPU for dual monitors for a more encompassing overview.
Products at a Glance
How we chose the best GPU for Deep Learning
We’ve made our choices for the best GPU for Deep Learning based upon a combination of factors, starting with their general availability and cost-effectiveness. We’ve stuck primarily with Ada graphics cards due to the fact they are current-generation, but have included an Ampere option for those working with more limited budgets. All RTX GPUs are capable of Deep Learning with Nvidia on the whole leading the charge in the AI revolution, so all budgets have been considered here.
Product Reviews
- Incredibly powerful
- Tonnes of VRAM
- Leading CUDA cores
- Power hungry
- Pricey
The RTX 4090 takes the top spot as our overall pick for the best GPU for Deep Learning and that’s down to its price point and versatility. While far from cheap, and primarily marketed towards gamers and creators, there’s still a ton of value to this graphics card which make it well worth considering for any data-led or large language model tasks you have in mind. This graphics card is armed with 24GB GDDR6X VRAM and is built upon the AD102 die with a total of 16,384 CUDA cores, 512 texture mapping units, and 176 render output units on a 384-bit memory bus.
Now, it’s worth stating that all that power doesn’t exactly come all too easily. You can expect to pay $1,599 / £1,579, which while at the highest-end of Nvidia’s GeForce gaming lineup, is actually fairly affordable compared to some of the more dedicated server GPUs available. You’ll be able to harness the power of the RTX 4090 effectively for many different Deep Learning tasks but it’s not going to have the same optimization as a Hopper architecture card that’s built for it.
- Aggressive pricing
- 20% bump in CUDA cores
- Clocked faster than base RTX 4070
- Won't be able to be pushed too far
If you’re someone that just wants to play around with Deep Learning without the substantial hardware investment then an RTX 4070 Super should have enough under the hood to hold up. That’s especially true if you want to get creative, and that’s thanks to its 12GB GDDR6X VRAM and 20% more CUDA cores over the original. While Deep Learning thrives with higher VRAM, there’s no arguing that the 7,168 CUDA cores of the RTX 4070 Super has enough to handle demanding tasks.
This is especially true if you’re considering getting familiar with Nvidia cuDNN (Deep Neural Network) library for accelerated learning, fusion support, and the Expressive Op Graph API. This will mean things such as conversational AI and voice assistance, just to name a couple of things. The RTX 4070 Super may not be as powerful as other top-end cards, but considering its $599 price point, there’s a lot that can be done without breaking the bank too badly.
- Sub-$1,000 price tag
- 16GB GDDR6X
- Powerful performance
- The RTX 4090 is faster
The RTX 4080 Super could be the perfect GPU for Deep Learning if your budget can just about stretch to just under $1,000. Retailing from $999 for the Founders Edition model, this new Ada architecture refresh keeps all the core hardware specs of the once eye-watering MSRP of the RTX 4080 but knocks a full $200 off for good measure. That means you’re getting 16GB GDDR6X VRAM and a 256-bit memory bus on the AD103 die, but with slightly more CUDA cores and an ever-so-slightly higher clock speed.
That’s right, the RTX 4080 Super features a total of 10,240 CUDA cores (up from the base RTX 4080’s 9,728) and a base clock speed of 2,295 Mhz. While there’s no VRAM upgrade, you should see this memory pool work a little harder for you thanks to iterative upgrades meaning a difference of between 2-5%. Considering the sub-$1,000 MSRP, this is the best GPU for Deep Learning second only to the RTX 4090, of which you’ll have to shell out considerably more.
- 24GB GDDR6X memory
- Still powerful in 2024
- Often found discounted from major retailers
- Previous generation
- Somewhat limited availability
While Ada is no doubt better at Deep Learning than Ampere, there’s no faulting the raw processing potential offered by the RTX 3090 even several years after its original introduction. That’s because the original BFGPU features a staggering 24GB GDDR6X memory and a massive 384-bit memory bus. Built on the GA102 graphics processor, it features a total of 10,496 CUDA cores which puts it in league with what the RTX 4080 can do but with a total of 8GB more VRAM at your disposal.
What’s more, the prices of the RTX 3090 now range around $1,099 and $1,399 depending on the partner card available from retailers such as Amazon which could save you a fair chunk of change if you don’t want to splash out for the newer RTX 4090. It’s a more cost-effective way of getting the same video memory which you’ll notice especially if you’re looking to engage in Deep Learning from a single GPU setup.
Things to consider with the best GPU for Deep Learning
Deep Learning is type of machine learning that uses as much data as a GPU or multiple GPUs can handle to extract things from the raw source. This could be through the construction of a large neutral network or generative models to supervised learning tasks for engineering jobs, too.
There are many complexities that go along with Deep Learning especially when creating neural networks by utilizing the the GPU’s memory and number of CUDA cores in advanced calculations when handling large datasets. While the original Nvidia Titan RTX was a frontrunner in this field, it’s since been outpaced by the likes of the RTX 40 series as it has greater GPU resources for Deep Learning algorithms. It all starts with parallel computing which takes a larger problem and solves them as smaller, simpler parts.
Think of it as machine assisted mathematics, such as Matrix operations. It starts with a scalar (single number) then evolves to a vector and then finally a matrix, which then leads to matrix multiplication. FLOPs (floating point operations) also play their part as metrics which can be used to make calculations in Deep Learning. Libraries include Pytorch, Tensorflow, Keras, Pandas, and NumPy, which use the GPUs’ chips for Deep Learning inference where the training is then implemented.
Is RTX better than GTX for Deep Learning?
Yes, RTX GPUs are better at Deep Learning than GTX predecessors due to the Ada architecture’s improvements, higher VRAM, and Tensor cores. Not to mention the increased power efficiency.
Is the RTX 4090 good for Deep Learning?
Yes, the RTX 4090 is a great choice as a GPU for Deep Learning thanks to its 24GB GDDR6X VRAM, tons of CUDA cores, and large memory bus, meaning it’s good as a single-GPU setup.
Our Verdict
The RTX 4090 takes the top spot as the best GPU for Deep Learning thanks to its huge amount of VRAM, powerful performance, and competitive pricing.