Tensor Core vs. CUDA Core: What’s the Difference?

Powerful Nvidia GPUs

Tensor Core vs. CUDA Core: What’s the Difference?

NVIDIA has done quite a bit to advance numerous technologies in the last few years. The GPU manufacturer has become a vanguard of sorts for artificial intelligence and machine learning. This is thanks in part to the development of their tensor and CUDA cores. However what is the difference between the two of these technologies? They both can help accelerate the processing necessary for AI and ML processes, but are they truly that different?

Let’s take a look at the technology and details behind both of these processing aids.

Tensor Core vs. CUDA Core: Side-By-Side Comparison

FeatureTensor CoreCUDA Core
AccuracyLower accuracyHigh accuracy
Compute SpeedFaster compute speedLower compute speed
Machine LearningSuited for machine learningCan handle machine learning, but not ideal for it
Use CaseLow-end and high-end grade AI developmentHigh throughput graphical processing
Use Cycle ReductionCan reduce use cycles for mathematical operationsCannot reduce use cycles
Operations Per Clock CycleMultiple operations per clock cycles1
Graphical ProcessingNot suited for graphical processingPurpose-built for graphical processing, powers modern NVIDIA GPUs.

Tensor Core vs. CUDA Core: What’s the Difference?

Tensor and CUDA cores both have the distinction of being proprietary technology developed by NVIDIA. Their overall use cases couldn’t be more different, however. Let’s take a look at some of the primary differences between the technologies.

Use Cases

CUDA cores have been a tenet of the NVIDIA GPU line since the Maxwell line was introduced in 2014. They provide a software supervision layer for access and work well for accelerating graphical processing. You can see CUDA cores in action with any PC running a modern NVIDIA GPU, from the GTX 900 series to the modern RTX 4000 series of cards. It isn’t developed entirely for machine learning or artificial intelligence processing purposes but can handle it with some ease compared to pure CPU-bound processing.

GeForce GTX 980MX
CUDA cores power PCs running NVIDIA GPUs, such as the GTX 980MX.


Tensor cores, on the other hand, are built for processing and crunching large computations. They were developed by NVIDIA for providing more robust mathematical processing compared to CUDA cores, and that can be seen in how it approaches individual mathematical operations. Where a CUDA core can handle a single calculation in a clock cycle, the tensor core can handle a multitude of them. This is thanks to the tensor core handling calculations via matrices, performing multiple successive or parallel computations in a single clock cycle. As you can imagine, this makes tensor cores a great fit for AI and ML development.

There are certainly enterprise-grade GPUs provided by NVIDIA for larger graphical and processing tasks, but if you’re in the business of AI, you’ll be wanting to handle things via tensor cores.

Machine Learning and AI

Machine learning and artificial intelligence have made some massive splashes in recent years. There have been constant news stories about recent developments and implementations of what was once more of a fringe technology. AI is big in quite a few industries, servicing customer service for retail, and stock exchanges, and powering the autonomous driving systems powering modern cars. Chances are if an AI developer isn’t offloading processing to a cloud-based managed service provider, they’re using NVIDIA-based processors.

Prior to the development and deployment of tensor cores, this was handled by CUDA cores. While CUDA cores handle these processes far faster than any native CPU due to the nature of their function, it isn’t an ideal solution. The tensor core was built solely by NVIDIA to provide high-speed processing and calculations for AI and ML-oriented development. They don’t handle graphical acceleration, but for their intended function it is barely a thought.

AI development handles large data sets and massive calculations, and as such been a time-consuming process. Tensor cores handle these data sets and numbers with aplomb. CUDA cores can certainly handle these, but they aren’t an ideal solution for it. Both cores can handle intensive numerical calculations at speeds far greater than your average CPU. That said, if you’re looking to build your own custom AI modeling rig, tensor cores are the way to go for optimal performance.

books about artificial intelligence
For AI modeling and machine learning, tensor cores offer the best performance.



Precision in computing refers to the level of accuracy depicted numerically by any calculations. With objects like your bog standard calculator, the accuracy isn’t high, being a rather anemic device compared to a modern PC. CUDA Cores are overwhelmingly precise, or highly accurate, in their depiction of numerical calculations. This is in stark contrast to how tensor cores handle precision. Mixed precision is the name of the game for tensor cores, and this does have benefits for its intended use case.

Tensor cores perform mixed precision modeling automatically, rapidly accelerating calculations at a rapid rate. This relates to a term called compute speed, a metric measured by how fast a processor can measure calculations. Tensor cores use matrices of mixed precision models to rapidly speed up the compute speed. Conversely, CUDA cores can only perform a single calculation per clock cycle.

If accuracy in numbers is truly important to the work you’re doing, then CUDA cores are the better choice. For those looking to crunch down massive amounts of calculations, tensor cores are the only logical choice.

Tensor Core vs. CUDA Core: Must-Know Facts

Tensor Core

  • NVIDIA’s response to usage of their GPUs in AI development
  • The fastest way to handle large data sets without custom hardware
  • The fourth generation of hardware supports six concurrent precision models
  • Tensor cores aid with ray tracing on NVIDIA hardware

CUDA Cores

  • One of the driving forces behind modern NVIDIA GPUs for graphical processing
  • Despite slower calculation time, it works in conjunction with thousands of parallel cores
  • CUDA cores well suited for cryptographic hashes, physics calculations, and game graphical rendering

Tensor Core vs. CUDA Core: Which One Should You Use?

With such a heady subject in mind, which suits your needs? If you’re just after something to make your games look great, then you’ll be leveraging some degree of tensor core and CUDA core at the same time. This is doubly true if your gaming rig is running a modern RTX graphics card and performing ray tracing. In the context of graphical acceleration, they work closely together but serve vastly different tasks.

If you’re looking for hardware to accelerate your AI rigs, then tensor cores are a great choice. NVIDIA has caught on to this and now makes specialty cards running tensor cores like the NVIDIA A100. With a staggering 80 GB of VRAM, this hot-rodded enterprise-grade card can handle up to 2 TB a second of data to run increasingly complex AI and ML models.

CUDA cores still have their place in AI and are plenty fast if you’re not in the business of developing massive AI models. For those looking to research independently, or are running a smaller AI shop, high-end consumer cards might work just as well for handling tasks. They might take more time, but they can pull their weight ably.

Tensor Core vs. CUDA Core: What’s the Difference? FAQs (Frequently Asked Questions) 

Are tensor cores and CUDA cores used for other purposes?

Tensor cores and CUDA cores could very easily be leveraged for mathematically heavy concepts like cryptographic hashing. Hashing requires intensive means to handle the massive numbers used to seed the hash itself. As such, having a high-speed and precise external processor is only a benefit for hashing.

Aside from that, you’ll see CUDA cores and tensor cores alike being used for graphical acceleration and adding all the bells and whistles you expect in modern PC gaming.

What can be AI used for?

Aside from heavier lifting like automation and autonomous driving, AI sees uses in many other fields. The most common usage you might see is if you work professionally in media. If you’ve ever leveraged a denoising algorithm, you’ve used an AI of a sort in your work.

Spam filters and automatic task handling for emails also operate off of AI. Users even get to train the model themselves by specifying additional messages which are spam or harmful so the model can further recognize useful messages.

Can home users do work in AI?

Work on AI and ML has certainly been done by hobbyist researchers. They may not have the means of your average massive tech conglomerate to spend on the hardware, but many have done just fine with average consumer-grade hardware.

Processing will undoubtedly take far more time, but if you have the patience you can engage in this sort of work while wearing sweats.

Does AMD offer anything similar to tensor cores?

Tensor and CUDA cores are both proprietary technologies developed by NVIDIA, and as such, there isn’t really much on the market that compares. There are certainly enterprise-grade hardware units made exclusively for processing AI models, but they are extremely expensive.

In the home space, AMD GPUs aren’t leveraging the same technologies for ray tracing or dynamic resolution sharpening. AMD might have other options for sharpening the image and offering greater fidelity, but nothing which directly compares to what NVIDIA has on the market currently.

Do tensor cores have other uses?

While primarily developed for AI modeling, tensor cores have been on the most recent NVIDIA GPUs. In a graphical acceleration setting, they aren’t ideal for things like polygons or anti-aliasing. Instead, where they focus on is providing things like ray-tracing. Dynamic resolution swapping is another area where they can excel, providing ample overhead to handle and maintain consistent framerates.

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