CUDA Cores vs Tensor Cores: What’s the Difference?

What are CUDA Cores?

CUDA Cores are the general-purpose processing units inside an NVIDIA GPU. Unlike a CPU, which has a small number of powerful cores, GPUs contain thousands of lightweight CUDA Cores designed to run simple operations in massive parallel batches. This makes them extremely efficient for workloads where the same operation must be repeated across large datasets.

CUDA Cores executes a wide range of tasks including FP32/FP64 arithmetic, integer operations, vector calculations and parallel loops. Developers usually interact with them through the CUDA programming model, which allows GPU kernels to launch thousands of threads at once.

They are essential for workloads like image processing, graphics rendering, simulations, data transformations and parts of machine learning pipelines that aren’t fully matrix-multiplication-heavy.

What are Tensor Cores?

Tensor Cores are specialised processing units introduced by NVIDIA to accelerate the matrix multiplications that power modern AI. While CUDA Cores can perform these operations, Tensor Cores are designed specifically for high-throughput linear algebra, the foundation of neural networks. They deliver massive speed-ups for operations like GEMM (General Matrix Multiply), convolution layers, attention blocks and transformer operations.

Tensor Cores work with mixed-precision formats such as FP16, BF16, FP8 and INT8. This allows AI models to run faster while maintaining accuracy. And this is exactly why they are a must-have for training and inference, often in deep learning frameworks like PyTorch and TensorFlow.

To compare with CUDA Cores, Tensor Cores offer higher FLOPs for AI workloads. For example, GPUs like the NVIDIA A100 and NVIDIA H100 rely heavily on Tensor Cores to achieve their industry-leading performance in large-scale training, generative AI and LLM inference.

What are the Key Differences Between CUDA Cores and Tensor Cores

CUDA Cores and Tensor Cores work together inside modern NVIDIA GPUs but they are built for completely different types of computation:

CUDA and Tensor Cores for Training and Inference

Understanding the difference between CUDA Cores and Tensor Cores becomes clearer when you look at how they behave in real-world AI workflows. To give you an idea:

Training a Transformer Model

During training, almost every major component including multi-head attention, feed-forward layers and projection layers involves large FP16 or BF16 matrix multiplications. Tensor Cores accelerate these computations, giving you:

• Faster epoch times
• Higher tokens-per-second
• Better training cost efficiency

Inference for LLMs or Vision Models

Inference workloads also depend heavily on fast matrix multiplication, especially at smaller batch sizes. Tensor Cores accelerate FP16, FP8 or INT8 inference, which reduces latency for models such as Llama 3.3, Stable Diffusion and more.

This means you get:

• Faster token generation
• Lower GPU utilisation
• Higher throughput per GPU

Conclusion

If your workloads depend on CUDA Core parallelism or Tensor Core acceleration, the right GPU can reduce training time, lower inference costs and help you ship products faster. Hyperstack offers a real cloud environment optimised for AI with on-demand access to high-performance NVIDIA GPUs including the NVIDIA RTX A6000, NVIDIA A100, NVIDIA H100 PCIe/SXM and more.

No matter if you’re training large transformer models, running FP8 inference pipelines or deploying production-scale generative AI, you can spin up the GPU VM you need in minutes. Our cloud GPU VMs offer high throughput and low-latency networking in an optimised environment, so you spend more time building and less time configuring.

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FAQs

What are CUDA Cores?

CUDA Cores are general-purpose processing units inside NVIDIA GPUs. They handle parallel compute tasks such as FP32/FP64 arithmetic, integer operations, graphics rendering, simulations and parts of machine learning pipelines that don’t rely on matrix multiplications.

What are Tensor Cores?

Tensor Cores are specialised GPU units designed to accelerate matrix multiplications for AI workloads. They work with mixed-precision formats like FP16, BF16, FP8 and INT8 for faster training and inference for deep learning models and LLMs.

What is the difference between CUDA Cores and Tensor Cores?

CUDA Cores are versatile and handle general-purpose parallel tasks, while Tensor Cores are optimised for AI-specific computations. Tensor Cores deliver higher FLOPs for matrix-heavy operations, whereas CUDA Cores manage broader workloads including preprocessing and rendering.

How many CUDA Cores does the NVIDIA A100 GPU have?

The NVIDIA A100 GPU features 6,912 CUDA Cores, providing massive parallel processing power for both AI and general-purpose workloads.

How many Tensor Cores does the NVIDIA A6000 GPU have?

The NVIDIA A6000 GPU includes 336 Tensor Cores, which accelerate AI matrix computations while the CUDA Cores handle general compute tasks.

Why are Tensor Cores important for AI training and inference?

Tensor Cores provide high-throughput acceleration for matrix multiplications, reducing training times, improving inference speed and enabling mixed-precision computation without sacrificing model accuracy.