◦ Optimized configs
◦ Industry-leading support
GPU → Tensor Core
What are tensor cores?
Tensor cores were introduced by NVIDIA in 2017 with their Volta GPU architecture, starting with the Tesla V100 — designed to accelerate matrix operations for machine learning and AI. Since then, tensor cores have become a standard feature in NVIDIA’s GPUs, revolutionizing … just about everything.
Learning about tensor cores can help professionals optimize software for modern GPUs, essential for AI, gaming, and data-heavy tasks. Whether you’re into cutting-edge computing or just curious about how GPUs work, understanding tensor cores unlocks serious performance potential.
What is a tensor core?
A tensor core is a specialized processing unit within a GPU, designed to accelerate matrix operations, such as those used in AI, deep learning, and high-performance computing tasks. These cores are a key feature of modern GPUs, significantly improving the performance of artificial intelligence (AI).
Tensor core benefits
Tensor cores offer several benefits.
- Speed: Tensor cores dramatically enhance the performance and throughput of AI workloads, enabling researchers and developers to train and deploy models more efficiently. This acceleration can lead to significant time and cost savings, making tensor cores an invaluable asset for AI professionals.
- Precision and efficiency: Tensor cores improve accuracy in calculations through mixed-precision operations. By combining lower-precision data with higher-precision accumulation, they achieve results similar to higher-precision calculations while reducing memory usage and computational demands.
- Future-proofing: Tensor cores provide a future-proof investment in AI infrastructure. As AI models become more complex, tensor cores can handle the increased demand for matrix operations, ensuring top-notch performance and compatibility with the latest AI technologies.
How do tensor cores work?
Key components of a tensor core include:
- Specialized hardware units
- Accelerate matrix operations
- Optimize large-scale matrix operations
- Improve precision and accuracy through mixed-precision operations
- Future-proof investment for AI infrastructure
The architecture of tensor cores revolves around mixed-precision matrix multiply-accumulate (MAC) operations. This means they can efficiently perform matrix multiplications and additions at the same time, using reduced-precision formats. This mixed-precision approach is how tensor cores achieve higher throughput and computational efficiency compared to traditional GPU cores.
Matrix operations form the backbone of AI computations, utilized in tasks such as deep learning, neural networks, and machine learning. These operations involve multiplying and adding matrices, which can be intensive and time-consuming, but tensor cores accelerate these matrix operations.
Here’s a quick rundown of how tensor cores accelerate AI computations:
- The input matrices for AI algorithms are loaded into the tensor cores.
- Tensor cores perform mixed-precision matrix multiply-accumulate operations on these matrices.
- The resulting output matrix is stored in memory.
- The accelerated computation is then used for subsequent calculations or further analysis.
By offloading the heavy matrix computations to tensor cores, GPUs significantly speed up workloads. This acceleration is particularly beneficial for deep learning applications, where large matrices and complex calculations are the norm.
The evolution of tensor cores
In their relatively short history, tensor cores have been through four generations.
Volta generation (2017)
The first generation of tensor cores debuted with NVIDIA’s Volta architecture in 2017, starting with the Tesla V100 GPU. These tensor cores were designed specifically to accelerate deep learning workloads by performing mixed-precision matrix multiplications. Volta introduced tensor cores as a breakthrough for AI, enabling dramatic speedups in neural network training and inference compared to traditional GPU cores.
Turing generation (2018)
Turing tensor cores expanded on Volta’s capabilities to include inference tasks where lower precision is acceptable, such as real-time object detection. Turing also brought tensor cores into consumer GPUs for the first time, enabling enhanced gaming performance and visuals.
Ampere generation (2020)
The Ampere tensor cores offered significant improvements in performance and efficiency. They introduced support for scientific computing and introduced “sparsity acceleration,” which leverages sparse matrix operations to double performance in certain AI workloads. This generation also provided better energy efficiency, making it ideal for both training massive AI models and running inference at scale.
Ada Lovelace generation (2022)
The Ada Lovelace tensor cores further enhanced AI performance with improved sparsity support and increased computational throughput. They also refined DLSS technology by using AI to generate entire frames — significantly boosting gaming frame rates. These tensor cores pushed the boundaries of real-time ray tracing and AI-driven graphics.
Prerequisites for using tensor cores
Prior to utilizing tensor cores, several prerequisites regarding hardware, software, and system configurations should be considered. Meeting these prerequisites ensures optimal performance and compatibility.
1. Hardware requirements for utilizing tensor cores
To harness the power of tensor cores, your system first needs compatible hardware. Specifically, tensor cores are found in NVIDIA GPUs based on the NVIDIA Ampere architecture. These GPUs feature specialized cores designed to accelerate matrix multiplication operations, making them ideal for deep learning and AI tasks.
2. Software dependencies and libraries for tensor core support
The appropriate hardware is crucial, but you also need the necessary software dependencies and libraries. NVIDIA provides the CUDA toolkit, which includes libraries like cuBLAS, cuDNN, and TensorRT, all optimized for accelerating deep learning tasks with tensor cores.
3. Recommended system configurations for optimal performance
For the best performance, make sure your system configuration aligns with the hardware and software requirements. This means securing sufficient GPU memory, fast storage, and a powerful CPU to complement the GPU’s capabilities. And make sure your system has the latest drivers installed to take advantage of any performance optimizations and bug fixes.
Tensor cores vs CUDA cores
In the realm of GPU computing and machine learning, tensor cores and CUDA cores are frequently discussed. While both are vital for accelerating computations, there are distinct characteristics and use cases.
Tensor cores are specialized hardware units in NVIDIA GPUs designed to efficiently perform matrix operations. These cores are optimized for deep learning workloads, enabling faster training and inference of neural networks. Tensor cores provide significant performance gains by performing mixed-precision computations, using lower precision formats for matrix calculations without sacrificing accuracy.
CUDA cores are the general-purpose parallel processors in NVIDIA GPUs. They handle a wide range of tasks, and can execute multiple instructions simultaneously, making them versatile for various computational workloads. CUDA cores execute the instructions generated by the CUDA programming model, allowing developers to leverage GPU power for parallel computing.
| Tensor cores | CUDA cores | |
|---|---|---|
| Functionality | Specialized hardware designed for deep learning tasks | General-purpose parallel processors |
| Performance | Optimized for matrix operations, providing faster performance in deep learning workloads | Provide high performance for a wide range of computational tasks |
| Flexibility | Specifically designed for AI and deep learning applications | Can be utilized for various computational tasks beyond deep learning |
| Programming | Requires utilizing specialized libraries and frameworks like TensorFlow and PyTorch | Can be programmed using CUDA programming language |
Tensor cores excel in machine learning applications heavily reliant on matrix operations, such as deep learning frameworks like TensorFlow and PyTorch. They accelerate training and inference by efficiently performing matrix multiplications and convolutions.
By contrast, CUDA cores are essential for general-purpose GPU computing tasks, including scientific simulations, data analytics, and rendering.
While tensor cores and CUDA cores possess different strengths and applications, they often collaborate to accelerate complex computations in machine learning and GPU computing. The combination of these cores unleashes the full potential of NVIDIA GPUs, enabling faster and more efficient processing for a variety of workloads.
What tensor cores are used for
In the field of AI, tensor cores have been widely adopted to expedite deep learning tasks.
- Autonomous driving: Tensor cores enable real-time object detection and recognition, allowing self-driving cars to accurately identify pedestrians, vehicles, and traffic signs.
- Healthcare: Tensor cores assist in medical image analysis, helping doctors detect and diagnose diseases more efficiently.
- Image and speech recognition: Tensor cores process vast amounts of data simultaneously to accelerate image and speech processing. This capability is particularly useful in fields like e-commerce for product recommendations or in voice-controlled devices for quick and accurate speech recognition.
Looking ahead, tensor cores hold huge potential for future advancements.
- AI: As AI continues to evolve, tensor cores will play a pivotal role in training more complex neural networks. Their high-performance computing capabilities will drive advancements in natural language processing, robotics, and virtual reality.
- Scientific research: Tensor cores will facilitate breakthroughs in scientific research, enabling the processing and analysis of massive datasets in fields like astronomy, climate modeling, and drug discovery.
GPU server hosting: The bridge between your budget and your server needs
Renting a GPU with tensor cores from a hosting provider offers several benefits, especially for those needing high-performance computing without significant upfront investment:
- Access to high-performance hardware: Renting gives you access to cutting-edge GPUs with tensor cores without purchasing expensive hardware.
- Scalability: Scale resources up or down based on your workload. Whether you’re training a large AI model or running smaller inference tasks, you can adjust your GPU usage to meet your needs.
- Cost efficiency: In addition to avoiding the upfront costs of buying hardware, GPU server hosting reduces expenses related to maintenance, upgrades, and energy consumption.
- Global accessibility: Hosted GPUs can be accessed from anywhere, allowing remote teams to collaborate efficiently and process workloads in the cloud without being tied to specific locations.
- No maintenance: Hosting providers handle hardware maintenance, updates, and potential failures, ensuring uninterrupted performance while you focus on your tasks.
- Optimized environments: Many providers offer pre-configured environments optimized for deep learning, machine learning, and other GPU-intensive tasks, saving you time on setup and configuration.
This flexibility, cost-effectiveness, and access to powerful hardware make renting a GPU with tensor cores an excellent option for professionals in AI, gaming, scientific research, and more.
Getting started with tensor core GPUs
Liquid Web’s GPU server hosting stands out for high-performance hardware (featuring advanced NVIDIA GPUs like the L4 and H100 NVL), paired with powerful AMD EPYC CPUs, and high-speed DDR5 memory for handling demanding workloads such as AI and machine learning.
Our pre-configured AI/ML software stack supports popular frameworks like TensorFlow and PyTorch, simplifying deployment and development. Additionally, our scalable and customizable server options ensure flexibility to meet various performance needs and adapt to growing business demands.
Click below to explore GPU server options or start a chat to talk to one of our experts about GPU hosting!
Additional resources
What is a GPU? →
What is, how it works, common use cases, and more
What is GPU memory? →
Why it’s important, how it works, what to consider, and more
GPU vs CPU →
What are the core differences? How do they work together? Which do you need?
Luke Cavanagh, Strategic Support & Accelerant at Liquid Web, is one of the company’s most seasoned subject matter experts, focusing on web hosting, digital marketing, and ecommerce. He is dedicated to educating readers on the latest trends and advancements in technology and digital infrastructure.