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
|Feature||Tensor Core||CUDA Core|
|Accuracy||Lower accuracy||High accuracy|
|Compute Speed||Faster compute speed||Lower compute speed|
|Machine Learning||Suited for machine learning||Can handle machine learning, but not ideal for it|
|Use Case||Low-end and high-end grade AI development||High throughput graphical processing|
|Use Cycle Reduction||Can reduce use cycles for mathematical operations||Cannot reduce use cycles|
|Operations Per Clock Cycle||Multiple operations per clock cycles||1|
|Graphical Processing||Not suited for graphical processing||Purpose-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.
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.
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.
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
- 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
- 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.
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