As we have entered the era of AI, the field of Machine learning has also started to rise and choosing the right hardware for Machine learning is quite confusing.
But, let me help you here. Machine learning isn’t about putting all the expensive stuff into one place.
With almost 3 years of experience, I have made the list of the best graphics cards for machine learning ranging from budget friendly, expensive, and mid ranged.
Our Recommendations For The Best Graphics Cards For Machine Learning
MSI Suprim Liquid X GeForce RTX 4090
Staff Pick
However, the normal edition will not suffice for machine learning tasks. The MSI Supreme Liquid X RTX 4090 had significantly outperformed the funder addition RTX 4090. While I was conducting Machine learning tasks on both of them, the MSI Supreme Liquid X RTX 4090 was better than the RTX 4090 due to temperature.
When it comes to specs. The MSI Supreme Liquid X RTX 4090 will never let you down. The card consists of 24GB VRAM which is great for high workloads and Nvidia combined it with 1,008GB/s of bandwidth making it impressive.
The card has 512 fourth-generation Tensor cores that enable the MSI Supreme Liquid X RTX 4090 to do dynamic calculations and mixed precision computing. These cores are very powerful to accelerate the overall performance while maintaining the accuracy.
Looking at the MSI Supreme Liquid X RTX 4090. The main advantage that it has over the normal RTX 4090 is its 240mm additional radiator. Fitting it up in a compact case can be a hedic but it is worth paying extra bucks over the standard RTX 4090.
Pros
- The MSI Supreme Liquid X RTX 4090 consists of 24GB of VRAM combined with 1,008GB/s of bandwidth.
- Its 512 fourth-generation Tensor cores for AI tasks make it a great GPU for Machine learning.
- As in Machine learning the GPU has to go under a high workload. So 240mm radiator comes to tackle the high temperature.
Cons
- Coming to the cons of MSI Supreme Liquid X RTX 4090 the main con would be its power consumption especially while working on high load so a minimum of 600 PSU power supply is a must.
- Another con in my opinion is that the card has an additional 240mm radiator for cooling that can be difficult to manage in small cases.
MSI Gaming X Slim GeForce RTX 4070 Ti
Budget Pick
Machine learning on a budget can be difficult. The MSI Gaming X Slim RTX 4070 Ti might be a great high-end gaming card. But when entering the field of Machine Learning these cards are good for entry level.
Plus, I have named these cards as entry-level because of the 240 fourth-generation Tensor cores, specifically designed for AI tasks.
In case you’re wondering, does the MSI Gaming X Slim RTX 4070 Ti lack somewhere? In my testing, the MSI Gaming X Slim RTX 4070 Ti started to lack a high workload. Its 12GB of GDDR6X VRAM with 504.2 GB/s of bandwidth may sound great for gaming but it lacks in the field of machine learning.
The one major reason that MSI Gaming X Slim RTX 4070 Ti made it to the list is that. In the price bracket that it comes. It easily beats its competitors due to its 240 fourth-generation Tensor cores.
MSI Gaming X Slim RTX 4070Ti has 3 fans and consists of a big heat sink that smoothly handles the heat when the card is under a huge stress load.
Pros
- The main advantage that MSI Gaming X Slim RTX 4070 Ti has over its competitors is its price range. It offers 240 fourth-generation Tensor cores which are enough to start with machine learning.
- The MSI Gaming X Slim RTX 4070 Ti is a 3-fan addition card and consists of a big Heat seal. That handled the heat well in my testing.
Cons
- The main downside I faced during testing was that the 12GB of GDDR6X VRAM started to lack on high loads.
- Another main downside is that the MSI Gaming X Slim RTX 4070 Ti is a three-fan addition card making it huge to fit in a small case.
Nvidia H100 Tensor Core
Best Pick
If money is not a problem and you want the best of the best in the field of Machine learning, The Nvidia H100 solves the problem. Consisting of 80TB of VRAM, 456 Tensor Cores, the card is purposely built to be an AI-focused professional graphics card.
Luckily I had the chance to test it myself. The Nvidia H100 is a beast when it comes to machine learning. But the only problem it had was it requires a proper workstation to run and cooling it down is also a big challenge to be faced.
Generally, the main reason behind the making of H100 was to build an AI-focused card. So the H100 got the newest generation 456 underscores. Which are built to perform complex calculations with high accuracy. Its 80TB of VRAM will sound huge to you but when under a huge load of machine learning this huge number of VRAM is required.
So to conclude the Nvidia H100. It is the best of the best that the GPU market is currently offering specifically for the field of AI and its sub-branches such as machine learning.
Pros
- The main advantage of the Nvidia H100 is that it has 80TB of VRAM making it more than enough.
- The 456 Tensor Cores especially built for AI work also make the Nvidia H100 the best option for Machine learning.
- Another advantage that the Nvidia H100 has. It supports PCIe 5.0 for faster connections to the motherboard.
Cons
- One major downside to the Nvida H100 is that it does not have any fans. So I had to rely on server fans to cool it down.
- Another main con would be its $40K price tag.
Wrap Up
Summing up, after a lot of testing, I will highly suggest a graphics card to be purchased from Nvidia. Always go for the card that has more VRAM and more AI-supported cores.
Moreover, pay some extra cash for the premium version of the card. The field of machine learning has a ton of load on the card.
So, it is always a good option to have a card that can tackle the heat more efficiently.
