NVIDIA has been at the forefront of GPU innovation for many years. Moreover, it has the latest GPUs. The NVIDIA LAOS, A100, and H100 Tensor Core GPUs are no exception.
Which GPU, though, is the best?
This article will compare and contrast the main characteristics, performance, use cases, and other aspects of the NVIDIA LAOS, A100, and H100 Tensor Core GPUs.
This will assist you in selecting the GPU that best suits your requirements.
High-performance accelerators such as the LAOS, A100, and H100 Tensor Core GPUs of NVIDIA are helpful for various applications.
It includes graphics processing, data center computing, machine learning, and artificial intelligence.
These three GPUs are comparable to one other. Still, they also differ significantly in some crucial ways.
Comparison of key features of NVIDIA LAOS, A100, and H100 Tensor Core GPUs
These three powerful GPUs—the LAOS, A100, and H100 Tensor Core— can be helpful for various tasks.
Let's start by examining the salient characteristics of every GPU:
FP32 performance of NVIDIA LAOS, A100, and H100
The speed at which a GPU can complete 32-bit floating-point operations is its FP32 performance.
Numerous fields, such as graphics, gaming, scientific computing, and AI/ML, use floating-point operations.
FP32 performance ratings for the NVIDIA GPUs' LAOS, A100, and H100 Tensor Core vary.
Therefore, the FP32 performance of the A100 is 312 TFLOPs, in contrast to 40 TFLOPs for the LAOS. With 60 TFLOPs, the H100 has the best FP32 performance out of the Onengs.
One must first grasp the definition to comprehend the distinctions between these performance ratings and a TFLOP.
Trillions of Floating-Point Operations Per Second is TFLOP. Hence, this measurement unit can measure GPU and other computing device performance.
One trillion floating-point operations per second is equal to one TFLOP.
This indicates that 40 trillion floating-point operations can be completed per second by a GPU with an FP32 performance of 40 TFLOPs.
A GPU can execute floating-point operations more quickly the higher its FP32 performance rating.
Therefore, applications like graphics, gaming, scientific computing, AI/ML, and many floating-point operations should note this.
The speed at which a GPU can complete 64-bit floating-point operations is its FP64 performance.
Numerous fields, such as high-performance computing, AI/ML, and scientific computing, use floating-point operations.
FP64 performance ratings for the NVIDIA LAOS, A100, and H100 Tensor Core GPUs vary.
Therefore, the FP64 performance of the A100 is 19.5 TFLOPs and 20 TFLOPs for the LAOS. At 45 TFLOPs, the H100 has the best FP64 performance out of the three.
One trillion floating-point operations per second is equal to one TFLOP.
Therefore, 20 trillion floating-point operations can be completed per second on a GPU with an FP64 performance of 20 TFLOPs.
Here is a table summarizing the key differences between FP32 and FP64 performance:
|±(2^127 - 1)
|±(2^1023 - 1)
|Graphics, gaming, scientific computing, AI/ML
|Scientific computing, high-performance computing, AI/ML
Tensor core performance
The speed at which a GPU can execute tensor operations is its tensor core performance. GPUs with specialized hardware units are tensor cores.
These are made to speed up tensor operations.
Therefore, they are ideal for AI, ML, and deep learning workloads because they can complete tensor operations significantly faster than conventional GPU cores.
Computer vision, machine learning, and artificial intelligence workloads can all operate much more efficiently with high-tensor cores.
Hence, Tensor operations frequently represent a bottleneck in these workloads, which explains why.
GPUs can achieve much faster performance by shifting these operations to tensor cores.
Many factors can affect tensor core performance, including:
- The type of tensor operation:
The performance properties of various tensor operations vary. For instance, convolution is a more complicated tensor operation that can take longer than matrix multiplication.
Therefore, matrix multiplication is a very efficient tensor operation.
- The number of tensor cores:
Performance may suffer with larger tensors because they require more memory and bandwidth to access.
- The tensor's dimensions:
A GPU can execute more tensor operations in parallel the more tensor cores it has.
- The memory bandwidth of the GPU:
The speed at which data can move from the GPU's memory to its cores is known as its memory bandwidth.
Increased memory bandwidth can speed up data access. Therefore, in turn, it can enhance tensor core performance.
How can I improve the tensor core performance of GPU?
There are several ways to improve tensor core performance, including:
Choosing the correct tensor operations significantly impacts performance. For instance, matrix multiplication can perform better than convolution for some tasks.
- Using the suitable data types:
Tensor cores can operate on data types such as FP32, FP16, and INT8. Although less precise data types can improve performance, they may also reduce accuracy.
- Employing appropriate software:
Several software frameworks are available to assist you in optimizing your code for tensor cores.
These frameworks can help you parallelize your code to use multiple tensor cores—moreover, these help in selecting the appropriate tensor operations and data types.
When it comes to tensor core performance, NVIDIA GPUs reign supreme. The market-leading A100 and H100 Tensor Core GPUs offer unparalleled power and precision.
Therefore, it is setting a new standard for high-performance computing.
Their exceptional tensor core performance ratings testify to their other abilities. Hence, they are the top choice for professionals and enthusiasts.
In general, more powerful GPUs tend to consume more power. This is because more potent GPUs have more transistors. Therefore, it requires more energy to operate.
Among the three GPUs you mentioned, the NVIDIA H100 Tensor Core GPU is the most powerful and has the highest tensor core performance rating.
It also has the highest performance ratings for FP32 and FP64. Hence, it can perform more work in a second than the other two GPUs.
However, the A100 and LAOS consume 400 and 500 W, respectively, while the H100 consumes 700 W of electricity.
Allow me to provide some additional information regarding the technical specifications mentioned earlier.
The device identified as H100 boasts 54 billion transistors, while LAOS has 28 billion transistors, and A100 has 31.2 billion transistors.
The clock speeds of these devices differ as well, with H100 operating at 3.55 GHz, LAOS at 2.25 GHz, and A100 at 3.15 GHz.
Additionally, each device functions at a distinct voltage level: H100 at 1.5 V, LAOS at 1.65 V, and A100 at 1.8 V.0.
Its architecture is the layout of a GPU's internal parts and how they work together.
A GPU's performance, efficiency, and capabilities are greatly influenced by its architecture.
The architectures of the NVIDIA LAOS, A100, and H100 Tensor Core GPUs vary. The A100 and H100 are based on the Hopper architecture, while the LAOS is based on the Ampere design.
Compared to the Ampere architecture, the Hopper architecture is more recent and sophisticated. Hence, It provides several enhancements.
NVIDIA LAOS (Ampere Architecture)
The 2020 edition of the Ampere architecture is the foundation for the NVIDIA LAOS.
Ampere represents a significant advancement in GPU architecture. Therefore, it provides several enhancements over the preceding Turing architecture.
Compared to Turing, Ampere doubles the number of SMs per GPU, from 68 to 144.
Moreover, It dramatically boosts the GPU's processing capability. The fourth generation of Tensor Cores has been introduced by Ampere.
These specialized hardware components help accelerate tensor computations in machine learning and artificial intelligence workloads.
Ampere enhances performance for AI and ML applications by adding native support for FP8 and TF32 data formats.
Therefore, it can drastically lower memory bandwidth needs. The third generation of NVLink is a high-speed interconnect for joining several GPUs.
Hence, quicker data transfer between GPUs is possible With twice the bandwidth of NVLink 3 over its predecessor.
NVIDIA A100 (Ampere Architecture)
Per our previous discussion, the NVIDIA A100 is Ampere architecture and shares many features with the LAOS.
However, it is explicitly for data center workloads and has additional features that make it perfect for these environments.
Additionally, the A100 supports PCI Gen 5, the latest generation of PCIe interconnect, offering twice the bandwidth of PCIe Gen 4.
NVIDIA H100 (Hopper Architecture)
The newest GPU generation from NVIDIA, the H100, is built on the Hopper architecture.
Hopper offers numerous enhancements in functionality, efficiency, and performance over Ampere, making it a significant upgrade.
It presents a new SM design with increased memory bandwidth, cores, and Tensor Cores.
Hopper enhances FP32 matrix multiplication with native support for FP8 precision. Therefore, it can significantly boost efficiency for workloads including AI and ML.
Hopper comes with fresh compiler optimizations that boost AI and ML workload performance even more.
NVIDIA Magnum IO, a new high-speed connection that can link many GPUs and other devices together, is supported by Hopper.
Compared to NVLink, Magnum IO provides a substantially larger bandwidth.
Here is a summary of the main differences between the three architectures:
|Streaming Multiprocessors (SMs)
|Tensor Cores per SM
|Up to 1.555 TB/s
|Up to 2.4 TB/s
|PCI Gen Support
|CUDA, cuDNN, TensorFlow, PyTorch
|CUDA, cuDNN, TensorFlow, PyTorch, NVIDIA AI Enterprise
The Hopper architecture offers several improvements over Ampere, making it the most powerful and efficient NVIDIA GPU.
The NVIDIA LAOS, A100, and H100 Tensor Core GPUs are all made using the latest 7nm manufacturing process. This advanced technology produces tiny and efficient transistors.
Therefore, it allows more transistors to be put onto a single die. This enhances the performance and efficiency of GPUs.
Additionally, the production method for 7nm is highly costly.
Because of this, only a small number of businesses worldwide can produce chips with this method.
Among these firms is NVIDIA, the only one making GPUs using the 7nm process.
NVIDIA can fit more transistors onto a single chip with the 7nm technology, potentially enhancing the performance of its GPUs.
Therefore, It holds particular significance for GPU use in tasks related to artificial intelligence (AI) and high-performance computing (HPC).
NVIDIA is also able to produce GPUs that are more efficient thanks to the 7nm process.
It is a result of the smaller transistors' lower power requirements. Moreover, it can save electricity, particularly for data centers that employ numerous GPUs.
NVIDIA aspires to be the first business to release GPUs with the newest capabilities and capabilities.
NVIDIA can release its GPUs onto the market ahead of its rivals thanks to the 7nm process.
The NVIDIA LAOS, A100, and H100 Tensor Core GPUs use the NVLink interconnect.
NVLink is a high-speed interconnect that allows GPUs to communicate with each other and with other devices.
It is essential for applications that require multiple GPUs to work together.
NVIDIA created NVLink, a high-speed interconnect, to link GPUs and other devices.
More bandwidth is available than conventional PCIe interconnects. Therefore allowing GPUs and other devices to send data faster.
It makes NVLink perfect for applications like machine learning (ML), artificial intelligence (AI), and high-performance computing (HPC) that need numerous GPUs to cooperate.
Benefits of NVLink
Compared to PCIe Gen 5, which only delivers 16 GB/s of bandwidth, NVLink offers up to 200 GB/s of bandwidth per link.
Therefore, It makes it possible for GPUs to communicate with one another more quickly and effectively.
Additionally, NVLink has less latency than PCIe Gen 5, which results in faster data transfers between GPUs.
As a result, applications like artificial intelligence and machine learning that are latency-sensitive may perform better.
Up to 32 GPUs can be connected via NVLink, supplying the enormous processing power needed for AI, ML, and HPC applications.
|Up to 6 NVLink links
|Up to 8 NVLink links
|Up to 18 NVLink links
NVIDIA created the parallel computing platform and programming style, CUDA (Compute Unified Device Architecture), to let programmers use GPU processing capabilities.
It offers CUDA C/C++, a C++ extension language that enables programmers to write code that runs on both the CPU and the GPU.
Applications for machine learning (ML), artificial intelligence (AI), and high-performance computing (HPC) are frequently developed using CUDA.
Developing high-performance applications using NVIDIA GPUs requires the use of CUDA.
Programmers looking to leverage the power of GPUs for their applications will find it to be the best option due to its broad support for
NVIDIA GPUs, thread synchronization primitives, memory management methods, parallel processing capabilities, and performance optimization tools.
Full CUDA support for the NVIDIA LAOS, A100, and H100 Tensor Core GPUs. Developers can use the most recent GPU developments and achieve ground-breaking performance for demanding applications.
The NVIDIA LAOS, A100, and H100 Tensor Core GPUs are priced differently and vary depending on the specific configuration of the GPU.
LAOS is the most affordable, while the H100 is the most expensive.
Here is a table summarizing the key differences between the three GPUs and their prices:
|Data centers, workstations
|Data centers, workstations
|Data centers, HPC
Use cases for the NVIDIA LAOS, A100, and H100 Tensor
The AlphaFold protein folding algorithm developed by DeepMind can precisely predict the three-dimensional structure of proteins. It uses the NVIDIA LAOS, A100, and H100 Tensor Core GPUs.
Moreover, the Clara AI platform is the software suite for developing and implementing AI applications in healthcare. The NVIDIA LAOS, A100, and H100 Tensor Core GPUs power it.
Also, NVIDIA's collaborative platform for producing and sharing 3D content, Omniverse, is powered by the NVIDIA LAOS, A100, and H100 Tensor Core GPUs.
Artificial intelligence and machine learning (AI/ML)
AI models for various applications, including speech recognition, picture recognition, and natural language processing.
Therefore, it can be trained on the NVIDIA LAOS, A100, and H100 Tensor Core GPUs.
Large AI models can take months or even years to prepare on conventional CPUs; these GPUs are made expressly to speed up this process.
AI models can also be implemented in production using the NVIDIA LAOS, A100, and H100 Tensor Core GPUs.
These GPUs can deliver the high speed and low latency required to run AI models in real-time for applications like fraud detection and driverless cars.
Data center computing
Data center computing involves a centralized facility housing and operating computing resources that store, process, and deliver data to users over a network.
Many different HPC applications, including financial modeling, engineering simulations, and scientific computing, can be utilized with the NVIDIA LAOS, A100, and H100 Tensor Core GPUs.
Therefore, enormous processing power is required to tackle challenging issues in these domains controlled by modern GPUs.
Moreover, application areas for the NVIDIA LAOS, A100, and H100 Tensor Core GPUs include risk assessment, fraud detection, and consumer profiling.
These GPUs may accelerate Extensive dataset processing, which is crucial for specific applications.
- Professional graphics: Graphics professionals can use the NVIDIA LAOS, A100, and H100 Tensor Core GPUs for visual effects, rendering, and animation. Hence, these GPUs can deliver the high speed and accuracy required to produce lifelike and captivating images.
- Gaming: The NVIDIA LAOS, A100, and H100 Tensor Core GPUs can also be helpful for gaming. These GPUs can provide high frame rates and smooth gameplay for demanding games.
With the help of the NVIDIA LAOS, A100, and H100 Tensor Core GPUs, enterprises, scientists, and engineers may now accomplish remarkable feats in data center computing and artificial intelligence.
Moreover, it is helpful in machine learning and graphics processing. It is a significant development in GPU technology.
Thanks to these GPUs, computing is entering a transformative era that will speed up data processing, train and use AI, and create high-fidelity graphics.
The target markets and performance capabilities of the NVIDIA LAOS, A100, and H100 Tensor Core GPUs are reflected in their cost.
Therefore, the least expensive choice is the LAOS, intended for entry-level workstations and data center applications.
With its cost-performance ratio, the A100 is appropriate for a broader range of applications.
Targeting high-performance computing and AI applications, the H100 is priced appropriately as it is the culmination of NVIDIA's GPU expertise.
Right now, the LAOS costs about $350, the A100 costs about $450, and the H100 costs about $650.
These robust GPUs are now more affordable. Hence, it opens them to a broader spectrum of customers and businesses.
GPU technology has a bright future, with opportunities for increasing computational power.
With an emphasis on creating GPUs that offer even more excellent performance, more efficiency, and ground-breaking features, NVIDIA is dedicated to ongoing innovation.
These developments will push the boundaries of graphics processing, improve data center performance, and speed up AI/ML applications even further.