Modern GPUs are the engines powering today’s computational breakthroughs—from lifelike gaming visuals to trillion-parameter AI models. NVIDIA GPUs have become indispensable tools for computational tasks. At the heart of these GPUs lie two critical components: CUDA Cores and Tensor Cores. While CUDA Cores are the workhorses for general-purpose computing, Tensor Cores specialize in accelerating AI and machine learning workloads. This guide explores their differences, performance, and ideal use cases—and explains how platforms like Novita AI empower users to harness both technologies seamlessly.
What Are CUDA Cores?
CUDA cores are the fundamental units responsible for parallel computation in NVIDIA GPUs. CUDA stands for Compute Unified Device Architecture, which is NVIDIA’s parallel computing platform and programming model. These cores handle a wide range of general-purpose tasks, including graphics rendering, simulations, and scientific computations.
Each CUDA core is designed to perform basic arithmetic operations (such as addition and multiplication) in parallel across a large dataset, allowing GPUs to handle complex tasks like 3D rendering or physics simulations much more efficiently than a CPU.
Applications of CUDA Cores:
- Graphics rendering (e.g., film production)
- Scientific simulations (e.g., physics, molecular biology)
- General-purpose parallel processing (e.g., large-scale data processing)
CUDA cores excel in tasks that can be broken down into smaller, independent operations that can run in parallel, making them perfect for a wide variety of computationally intensive workloads.
source from: https://www.nvidia.com/
What Are Tensor Cores?
Tensor cores, introduced by NVIDIA in the Volta architecture, are specialized cores designed to accelerate AI workloads, particularly deep learning tasks. These cores are optimized for matrix operations, which are central to neural networks. Tensor cores can process multiple operations simultaneously and are highly efficient when dealing with large-scale matrix multiplications and convolutions—critical tasks in training and inference of deep learning models.
Tensor cores are designed to handle mixed-precision arithmetic, meaning they can perform computations in lower precision formats (such as FP16 or INT8), which significantly boosts performance without compromising the accuracy required for deep learning tasks.
Applications of Tensor Cores:
- Neural network training (e.g., convolutional and recurrent neural networks)
- AI inference (e.g., object detection, language processing)
- High-performance deep learning (e.g., large language models like GPT)
Tensor cores are optimized for specific deep learning operations, such as matrix multiplications, which makes them ideal for workloads that involve training complex AI models or performing real-time inference.
This image illustrates the SM (Streaming Multiprocessor) architecture of an NVIDIA GPU, highlighting the Tensor Cores and their integration within the overall structure. The Tensor Cores are specialized units designed to accelerate matrix operations critical for deep learning tasks.
source from: https://www.nvidia.com/
How They Work: Technical Breakdown
The following table provides a technical comparison between CUDA Cores and Tensor Cores, highlighting their distinct functions, precision support, throughput, and energy efficiency. This comparison offers insights into how each core type contributes to different computational tasks, particularly in the context of AI and deep learning workloads.
| Aspect | CUDA Cores | Tensor Cores |
|---|---|---|
| Core Function | Execute scalar/vector operations (e.g., FP32 + FP32). | Optimized for matrix math (e.g., C=A×B+CC=A×B+C). |
| Precision Support | FP32, FP64 | FP16, INT8, BF16, FP8, FP4 (with FP32 accumulation). |
| Throughput | High for diverse parallel tasks. | 30x faster for matrix-heavy workloads (e.g., AI training). |
| Energy Efficiency | Optimized for sustained workloads (e.g., gaming). | 40% lower power consumption for AI tasks. |
Performance Comparison
While both CUDA cores and Tensor cores contribute to GPU performance, their roles and optimizations are suited to different workloads.
- CUDA Cores are great for general-purpose computing tasks like graphics rendering and scientific simulations. They are highly effective for parallel processing tasks that require handling large amounts of data simultaneously.
- Tensor Cores dramatically improve performance for deep learning models by handling matrix operations in parallel. These cores can achieve significantly higher throughput compared to CUDA Cores when it comes to AI-specific tasks.
Optimizing Your Workload: When to Use CUDA Cores vs Tensor Cores
When to Use CUDA Cores：
- General-purpose tasks that require high-throughput parallel processing, like graphics rendering or simulations.
- Workloads that don’t rely heavily on matrix operations but require efficient parallel computing.
When to Use Tensor Cores：
- Deep learning tasks that involve large-scale matrix multiplications, such as training neural networks.
- AI inference tasks where low-latency and high-throughput matrix operations are critical for real-time performance.
To get the best performance, many modern workloads benefit from a hybrid approach, utilizing CUDA cores for general tasks and Tensor cores for AI-specific operations.
Modern GPUs like the H100 combine both cores. For example:
- Use CUDA Cores for data preprocessing.
- Offload training to Tensor Cores for 30x speedups.
Why Choose Novita AI as Your GPU Cloud Provider?
Access to Both CUDA and Tensor Cores
Novita AI offers cloud-based GPU services that provide users with access to both CUDA cores and Tensor cores, allowing for flexible and efficient use of resources. Whether you are running general-purpose simulations or training AI models, Novita AI has the right GPU infrastructure to support your needs.
Scalability and Cost-Effectiveness
Novita AI allows users to rent GPUs on-demand, scaling up or down based on their computational requirements. This pay-as-you-go model eliminates the need for upfront hardware investments and offers flexibility for fluctuating workloads. Whether you’re working on a short-term AI project or a long-term simulation, Novita AI’s GPU cloud is a cost-effective solution.
Below is our comprehensive pricing structure for different GPU instances. We offer both on-demand hourly rates and subscription plans with increasing discounts for longer commitments. All plans include dedicated resources and premium support. Select your preferred option based on your computational needs and usage patterns.
| Option | RTX 3090 24 GB | RXT 4090 24 GB | RXT 6000 Ada 48GB | H100 SXM 80 GB |
| On Demand | $0.21/hr | $0.35/hr | $0.70/hr | $2.89/hr |
| 1-5 months | $136.00/month (10% OFF) | $226.80/month (10% OFF) | $453.60/month(10% OFF) | $1872.72/month (10% OFF) |
| 6-11 months | $129.00/month( (15% OFF) | $206.64/month (18% OFF) | $428.40/month(15% OFF) | $1664.64/month (20% OFF) |
| 12 months | $113.40/month(25% OFF) | $189.00/month (25% OFF) | $403.20/month(20% OFF) | $1498.18/month (28% OFF) |
Getting Started with Novita AI
Step1：Create an account
Ready to get started? Visit the Novita AI platform and create your account in just a few minutes. Once logged in, head to the ‘GPUs’ section where you can explore available instances, compare specifications, and choose the best plan for your computational needs. Our user-friendly interface makes it simple to deploy your first GPU instance and kickstart your AI development journey.
Step2：Select Your GPU
Our platform offers a wide range of professionally designed templates to suit your specific needs, while also giving you the flexibility to create your own from scratch. With access to powerful GPUs like the NVIDIA H100, equipped with ample VRAM and RAM, we guarantee fast, smooth, and efficient training for even the most complex AI models.
Step3：Customize Your Setup
Start with 60GB of free Container Disk storage and scale effortlessly as your needs grow. Select from flexible on-demand pricing or subscription plans to fit your budget and usage patterns. Whether you’re in development, testing, or full-scale deployment, our storage solutions scale seamlessly with your business. As your data footprint expands, you can instantly purchase additional storage space to keep up with your growing demands.
Step4：Launch Your Instance
Choose between “On Demand” or “Subscription” based on your needs and budget. Carefully review your selected instance configuration and pricing breakdown. With just a single click on “Deploy,” your GPU instance will be up and running, ready for immediate use.
Conclusion
Understanding the differences between CUDA cores and Tensor cores is essential for optimizing your GPU workload. CUDA cores are ideal for general-purpose parallel computing tasks, while Tensor cores excel at accelerating deep learning tasks. By leveraging both core types, you can maximize the performance of your GPU and optimize your workflows.
For those seeking flexible, high-performance GPU resources, Novita AI provides an excellent solution, offering access to both CUDA cores and Tensor cores in a scalable, cost-effective cloud environment. Whether you’re working on AI, simulations, or anything in between, Novita AI enables you to select the right GPU for your needs and optimize your computing performance.
Frequently Asked Questions
While Tensor Cores are specialized for AI tasks and deep learning, they are not ideal for general-purpose computing like CUDA Cores. Tensor Cores are optimized for matrix operations and mixed-precision calculations, which makes them more suited for training neural networks and running AI inference, rather than traditional computing tasks.
In modern GPUs like the NVIDIA A100, both CUDA Cores and Tensor Cores work together to handle different types of workloads. CUDA Cores take care of general tasks like data processing and graphics rendering, while Tensor Cores accelerate the matrix-heavy calculations needed for deep learning tasks, such as training large neural networks.
Use CUDA Cores for general computing tasks, such as data processing, scientific simulations, and tasks that don’t require heavy matrix operations. On the other hand, use Tensor Cores when working with AI workloads, especially deep learning tasks like training convolutional or recurrent neural networks, or when running large-scale AI inference models such as GPT.
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.
Recommended Reading
What Are Tensor Cores? The Key to Supercharging Your AI Models
What Are CUDA Cores? A Deep Dive Into GPU Parallel Processing
GPU Comparison for AI Modeling: A Comprehensive Guide
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