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GPU → CUDA Cores vs Tensor Cores
CUDA cores vs tensor cores: Architectures, differences, and best use cases
When it comes to GPU performance, not all cores are created equal. Whether you’re gaming, training AI models, or tackling high-performance computing, the type of cores inside your GPU server can make or break your workload.
NVIDIA’s CUDA cores and tensor cores both promise powerful acceleration, but they serve very different purposes. If you’ve ever wondered what sets them apart—and which one is the best fit for your needs—you’re in the right place.
Let’s break down the differences and see how CUDA cores vs tensor cores shapes the future of computing.
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What is a GPU?
A GPU (graphics processing unit) is a specialized circuit designed to efficiently process and use computer data in parallel. The GPU offloads these compute-intensive tasks from the CPU (central processing unit), to allow the latter to focus on more general workloads.
What is a CUDA core?
A CUDA core is a processing unit within a NVIDIA GPU that handles the efficient parallel processing. CUDA (Compute Unified Device Architecture) cores are analogous to CPU cores but much smaller, simpler, and optimized for executing more threads simultaneously. While a CPU typically has a handful of powerful cores optimized for sequential tasks, a GPU can have thousands of CUDA cores that break workloads into smaller pieces and process them concurrently.
At a high level, CUDA cores work by executing SIMT (Single Instruction, Multiple Threads) operations. This means multiple CUDA cores execute the same instruction across different pieces of data in parallel. This architecture is particularly well-suited for tasks like graphics rendering, scientific simulations, and other high-performance computing workloads.
Each CUDA core itself is relatively simple—it doesn’t have features like out-of-order execution or complex branch prediction found in CPU cores. Instead, CUDA cores rely on massive parallelism and high memory bandwidth to achieve performance gains.
When a program written in CUDA (NVIDIA’s parallel computing platform) runs on a GPU, it organizes tasks into grids, blocks, and threads. Each thread is assigned to a CUDA core and executes small portions of the computation simultaneously. That’s how a GPU can handle massive amounts of data at once.
CUDA core applications
A CUDA core GPU excels in workloads that demand high parallel processing power. Here are three ideal use cases:
Gaming graphics
CUDA cores play a critical role in real-time rendering for video games. Modern games rely on rasterization, where thousands (or millions) of pixels are processed simultaneously to generate realistic visuals.
CUDA Cores handle shading calculations, lighting effects, and texture mapping, all of which contribute to a game’s graphical fidelity. They also support post-processing effects, such as motion blur and ambient occlusion, which enhance realism.
While Ray Tracing Cores and Tensor Cores in NVIDIA RTX GPUs help with advanced lighting and AI upscaling (e.g., DLSS), the bulk of traditional game rendering still relies on CUDA Cores for high frame rates and smooth gameplay.
Physics simulations
CUDA cores are widely used in physics engines that simulate realistic object interactions in games and scientific applications. Many physics-based effects—such as fluid dynamics, cloth simulation, rigid body collisions, and smoke/fire effects—rely on CUDA acceleration.
In scientific research, CUDA cores accelerate computational fluid dynamics (CFD), molecular dynamics, and structural analysis, making them essential for engineering, medical simulations, and weather modeling.
Video processing
CUDA cores accelerate video encoding, decoding, and editing tasks. Video applications commonly leverage CUDA acceleration for video rendering, color grading, and visual effects processing.
GPUs with CUDA support allow for faster transcoding (e.g., converting videos from one format to another) by offloading computationally heavy tasks from the CPU. CUDA acceleration is also used in AI-powered video enhancements, such as upscaling (e.g., NVIDIA RTX Video Super Resolution), noise reduction, and motion interpolation for smoother playback.
General-purpose GPU (GPGPU) workloads
Beyond gaming and media, CUDA cores enable general-purpose computing (GPGPU), where a GPU is used for tasks traditionally handled by a CPU. This is particularly useful in financial modeling, cryptography, bioinformatics, and computational chemistry, where massive datasets need to be processed in parallel.
CUDA cores are also used in big data analytics and database acceleration, as GPUs can significantly speed up large-scale computations compared to CPUs alone. Many high-performance computing (HPC) applications leverage CUDA for parallelized simulations, numerical computations, and complex mathematical modeling.
What is a tensor core?
A tensor core is a specialized processing unit within NVIDIA GPUs designed to accelerate tensor operations, particularly matrix multiplications, that are fundamental to deep learning and high-performance computing. While CUDA cores handle general-purpose parallel workloads, tensor cores are optimized for mixed-precision computing, allowing them to perform massive amounts of floating-point calculations far more efficiently than traditional CUDA cores.
At a high level, tensor cores work by performing fused multiply-add (FMA) operations on matrices in a single step, rather than breaking them down into multiple instructions. This makes them ideal for deep learning workloads, where neural networks require extensive matrix multiplications and accumulation.
Tensor cores support lower-precision formats like FP16 (half-precision), BF16 (bfloat16), and INT8, while leveraging FP32 accumulation to maintain accuracy. This allows tensor cores to achieve much higher throughput without sacrificing too much precision.
Tensor cores are heavily utilized in AI and deep learning frameworks like TensorFlow and PyTorch, where they speed up matrix-heavy computations. Beyond AI, tensor cores also enhance scientific simulations, HPC workloads, and real-time ray tracing.
Because they provide a significant boost in performance compared to CUDA cores alone, tensor cores have become a key differentiator in NVIDIA’s RTX and data center GPUs.
CUDA cores vs tensor cores: Key differences
CUDA cores and tensor cores both play crucial roles in GPU computing, but they are designed for different types of workloads. Here are the key differences:
1. Purpose and workload optimization
CUDA cores are general-purpose parallel processors designed to handle a wide range of tasks, from graphics rendering to general compute operations. They excel at parallelizing workloads like rasterization, physics simulations, and traditional compute-heavy applications.
Tensor cores are specialized hardware units specifically designed to accelerate matrix multiplications, which are the foundation of deep learning and AI. They optimize deep learning workloads by executing tensor operations with much higher throughput than CUDA cores.
2. Precision and data types
CUDA cores primarily operate on single-precision (FP32) and double-precision (FP64) floating-point numbers, which makes them well-suited for a broad range of computations.
Tensor cores support mixed-precision arithmetic (FP16, BF16, INT8, INT4) with FP32 accumulation, which means they can process lower-precision computations much faster while maintaining acceptable accuracy—critical for deep learning training and inference.
3. Computational efficiency and speed
CUDA cores execute operations sequentially in parallel, meaning multiple cores perform calculations simultaneously but at a more granular level.
Tensor cores leverage fused multiply-add (FMA) operations to perform multiple calculations in a single clock cycle. This enables them to perform matrix multiplications several times faster than CUDA cores, particularly in AI and scientific computing workloads.
4. Use cases
CUDA cores are used for traditional parallel computing, such as gaming graphics, physics simulations, video processing, and general-purpose GPU workloads.
Tensor cores are optimized for AI, deep learning, and scientific computing, where large-scale matrix operations are critical. They significantly accelerate training for neural networks and inference for AI models.
5. Availability in NVIDIA GPUs
CUDA Cores are present in all NVIDIA GPUs, from entry-level consumer cards to high-end data center GPUs.
Tensor cores are found in RTX-series GPUs (e.g., RTX 30, 40 series), NVIDIA data center GPUs (A100, H100), and workstation-class GPUs, where AI acceleration is a priority.
| CUDA cores | Tensor cores | |
|---|---|---|
| Purpose and optimization | General-purpose parallel processing | Specialized for accelerating matrix multiplications |
| Precision | Primarily FP32 (single-precision) and FP64 (double-precision) | Supports mixed-precision (FP16, BF16, INT8, INT4) with FP32 accumulation |
| Speed | Executes operations sequentially in parallel | Fused multiply-add (FMA) operations for significantly faster matrix computations |
| Use cases | Gaming graphics, video processing, general-purpose GPU computing | Deep learning, AI model training/inference, scientific simulations, high-performance computing |
| Availability in NVIDIA GPUs | Present in all NVIDIA GPUs, from consumer to data center models | Found in RTX-series, data center GPUs (A100, H100), and workstation-class GPUs |
Tensor core vs CUDA core for AI and machine learning models?
When it comes to training and running AI programs and machine learning models, tensor cores are far superior to CUDA cores because of their optimized hardware. Tensor cores are specifically designed to accelerate the matrix multiplications and tensor operations that power neural networks.
In fact, tensor cores often reduce training times by 2x to 5x, compared to CUDA cores alone. This is especially important for large-scale deep learning models like transformers (e.g., GPT, BERT), CNNs (e.g., ResNet, EfficientNet), and diffusion models. The ability to quickly process lower-precision data means AI researchers and engineers can iterate models more quickly, leading to faster development cycles.
For AI inference (running trained models in production), tensor cores again provide a significant advantage by accelerating the operations involved in making predictions.
CUDA Cores are still necessary for general GPU computations and to support deep learning workloads to some extent, but they are far less efficient than tensor cores for AI tasks. If the primary use case is deep learning—whether for training or inference—tensor cores are the clear choice. They are a key reason why NVIDIA GPUs dominate AI workloads, as competing architectures often lack equivalent hardware acceleration.
Getting started with GPU servers
Which do you need? If you’re working on gaming, video editing, or general GPU-accelerated tasks, CUDA cores are probably sufficient. If you’re training AI models, performing large-scale simulations, or working with machine learning frameworks, tensor cores provide massive performance boosts and should probably be prioritized.
Once you’ve determined how much GPU power you need, it’s time to find the right GPU server hosting provider, and that’s where Liquid Web comes in. We’ve been providing industry-leading hardware, hosting services, and support for decades. Now, our expert teams are offering NVIDIA GPU server hosting as well.
Click below to explore options or start a chat with one of our dedicated server support team members today.
Additional resources
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